Computer architecture for maintaining a distance metric across correlithm objects in a correlithm object processing system

ABSTRACT

A device configured to emulate a correlithm object processing system includes a memory that stores a node table that identifies a plurality of source correlithm objects and a plurality of corresponding target correlithm objects. The system further includes a node coupled to the memory and configured to receive an input correlithm object, identify a source correlithm object from the node table with the shortest n-dimensional distance to the input correlithm object, and identify a first target correlithm object from the node table linked with the identified source correlithm object. The node further generates a second target correlithm object that is offset in n-dimensional space from the first target correlithm object by the distance between the input correlithm object and the identified source correlithm object. The node outputs the second target correlithm object.

TECHNICAL FIELD

The present disclosure relates generally to computer architectures foremulating a processing system, and more specifically to computerarchitectures for maintaining a distance metric across correlithmobjects in a correlithm object processing system.

BACKGROUND

Conventional computers are highly attuned to using operations thatrequire manipulating ordinal numbers, especially ordinal binaryintegers. The value of an ordinal number corresponds with its positionin a set of sequentially ordered number values. These computers useordinal binary integers to represent, manipulate, and store information.These computers rely on the numerical order of ordinal binary integersrepresenting data to perform various operations such as counting,sorting, indexing, and mathematical calculations. Even when performingoperations that involve other number systems (e.g. floating point),conventional computers still resort to using ordinal binary integers toperform any operations.

Ordinal based number systems only provide information about the sequenceorder of the numbers themselves based on their numeric values. Ordinalnumbers do not provide any information about any other types ofrelationships for the data being represented by the numeric values suchas similarity. For example, when a conventional computer uses ordinalnumbers to represent data samples (e.g. images or audio signals),different data samples are represented by different numeric values. Thedifferent numeric values do not provide any information about howsimilar or dissimilar one data sample is from another. Unless there isan exact match in ordinal number values, conventional systems are unableto tell if a data sample matches or is similar to any other datasamples. As a result, conventional computers are unable to use ordinalnumbers by themselves for comparing different data samples and insteadthese computers rely on complex signal processing techniques.Determining whether a data sample matches or is similar to other datasamples is not a trivial task and poses several technical challenges forconventional computers. These technical challenges result in complexprocesses that consume processing power which reduces the speed andperformance of the system. The ability to compare unknown data samplesto known data samples is crucial for many security applications such asface recognition, voice recognition, and fraud detection.

Thus, it is desirable to provide a solution that allows computingsystems to efficiently determine how similar different data samples areto each other and to perform operations based on their similarity.

SUMMARY

Conventional computers are highly attuned to using operations thatrequire manipulating ordinal numbers, especially ordinal binaryintegers. The value of an ordinal number corresponds with its positionin a set of sequentially ordered number values. These computers useordinal binary integers to represent, manipulate, and store information.These computers rely on the numerical order of ordinal binary integersrepresenting data to perform various operations such as counting,sorting, indexing, and mathematical calculations. Even when performingoperations that involve other number systems (e.g. floating point),conventional computers still resort to using ordinal binary integers toperform any operations.

Ordinal based number systems only provide information about the sequenceorder of the numbers themselves based on their numeric values. Ordinalnumbers do not provide any information about any other types ofrelationships for the data being represented by the numeric values suchas similarity. For example, when a conventional computer uses ordinalnumbers to represent data samples (e.g. images or audio signals),different data samples are represented by different numeric values. Thedifferent numeric values do not provide any information about howsimilar or dissimilar one data sample is from another. Unless there isan exact match in ordinal number values, conventional systems are unableto tell if a data sample matches or is similar to any other datasamples. As a result, conventional computers are unable to use ordinalnumbers by themselves for comparing different data samples and insteadthese computers rely on complex signal processing techniques.Determining whether a data sample matches or is similar to other datasamples is not a trivial task and poses several technical challenges forconventional computers. These technical challenges result in complexprocesses that consume processing power which reduces the speed andperformance of the system. The ability to compare unknown data samplesto known data samples is crucial for many applications such as securityapplication (e.g. face recognition, voice recognition, and frauddetection).

The system described in the present application provides a technicalsolution that enables the system to efficiently determine how similardifferent objects are to each other and to perform operations based ontheir similarity. In contrast to conventional systems, the system usesan unconventional configuration to perform various operations usingcategorical numbers and geometric objects, also referred to ascorrelithm objects, instead of ordinal numbers. Using categoricalnumbers and correlithm objects on a conventional device involveschanging the traditional operation of the computer to supportrepresenting and manipulating concepts as correlithm objects. A deviceor system may be configured to implement or emulate a special purposecomputing device capable of performing operations using correlithmobjects. Implementing or emulating a correlithm object processing systemimproves the operation of a device by enabling the device to performnon-binary comparisons (i.e. match or no match) between different datasamples. This enables the device to quantify a degree of similaritybetween different data samples. This increases the flexibility of thedevice to work with data samples having different data types and/orformats, and also increases the speed and performance of the device whenperforming operations using data samples. These technical advantages andother improvements to the device are described in more detail throughoutthe disclosure.

In one embodiment, the system is configured to use binary integers ascategorical numbers rather than ordinal numbers which enables the systemto determine how similar a data sample is to other data samples.Categorical numbers provide information about similar or dissimilardifferent data samples are from each other. For example, categoricalnumbers can be used in facial recognition applications to representdifferent images of faces and/or features of the faces. The systemprovides a technical advantage by allowing the system to assigncorrelithm objects represented by categorical numbers to different datasamples based on how similar they are to other data samples. As anexample, the system is able to assign correlithm objects to differentimages of people such that the correlithm objects can be directly usedto determine how similar the people in the images are to each other. Inother words, the system is able to use correlithm objects in facialrecognition applications to quickly determine whether a captured imageof a person matches any previously stored images without relying onconventional signal processing techniques.

Correlithm object processing systems use new types of data structurescalled correlithm objects that improve the way a device operates, forexample, by enabling the device to perform non-binary data setcomparisons and to quantify the similarity between different datasamples. Correlithm objects are data structures designed to improve theway a device stores, retrieves, and compares data samples in memory.Correlithm objects also provide a data structure that is independent ofthe data type and format of the data samples they represent. Correlithmobjects allow data samples to be directly compared regardless of theiroriginal data type and/or format.

A correlithm object processing system uses a combination of a sensortable, a node table, and/or an actor table to provide a specific set ofrules that improve computer-related technologies by enabling devices tocompare and to determine the degree of similarity between different datasamples regardless of the data type and/or format of the data samplethey represent. The ability to directly compare data samples havingdifferent data types and/or formatting is a new functionality thatcannot be performed using conventional computing systems and datastructures.

In addition, correlithm object processing system uses a combination of asensor table, a node table, and/or an actor table to provide aparticular manner for transforming data samples between ordinal numberrepresentations and correlithm objects in a correlithm object domain.Transforming data samples between ordinal number representations andcorrelithm objects involves fundamentally changing the data type of datasamples between an ordinal number system and a categorical number systemto achieve the previously described benefits of the correlithm objectprocessing system.

Using correlithm objects allows the system or device to compare datasamples (e.g. images) even when the input data sample does not exactlymatch any known or previously stored input values. For example, an inputdata sample that is an image may have different lighting conditions thanthe previously stored images. The differences in lighting conditions canmake images of the same person appear different from each other. Thedevice uses an unconventional configuration that implements a correlithmobject processing system that uses the distance between the data sampleswhich are represented as correlithm objects and other known data samplesto determine whether the input data sample matches or is similar to theother known data samples. Implementing a correlithm object processingsystem fundamentally changes the device and the traditional dataprocessing paradigm. Implementing the correlithm object processingsystem improves the operation of the device by enabling the device toperform non-binary comparisons of data samples. In other words, thedevice is able to determine how similar the data samples are to eachother even when the data samples are not exact matches. In addition, thedevice is able to quantify how similar data samples are to one another.The ability to determine how similar data samples are to each other isunique and distinct from conventional computers that can only performbinary comparisons to identify exact matches.

The problems associated with comparing data sets and identifying matchesbased on the comparison are problems necessarily rooted in computertechnologies. As described above, conventional systems are limited to abinary comparison that can only determine whether an exact match isfound. Emulating a correlithm object processing system provides atechnical solution that addresses problems associated with comparingdata sets and identifying matches. Using correlithm objects to representdata samples fundamentally changes the operation of a device and how thedevice views data samples. By implementing a correlithm objectprocessing system, the device can determine the distance between thedata samples and other known data samples to determine whether the inputdata sample matches or is similar to the other known data samples. Inaddition, the device is able to determine a degree of similarity thatquantifies how similar different data samples are to one another.

Certain embodiments of the present disclosure may include some, all, ornone of these advantages. These advantages and other features will bemore clearly understood from the following detailed description taken inconjunction with the accompanying drawings and claims.

BRIEF DESCRIPTION OF THE DRAWINGS

For a more complete understanding of this disclosure, reference is nowmade to the following brief description, taken in connection with theaccompanying drawings and detailed description, wherein like referencenumerals represent like parts.

FIG. 1 is a schematic view of an embodiment of a special purposecomputer implementing correlithm objects in an n-dimensional space;

FIG. 2 is a perspective view of an embodiment of a mapping betweencorrelithm objects in different n-dimensional spaces;

FIG. 3 is a schematic view of an embodiment of a correlithm objectprocessing system;

FIG. 4 is a protocol diagram of an embodiment of a correlithm objectprocess flow;

FIG. 5 is a schematic diagram of an embodiment a computer architecturefor emulating a correlithm object processing system;

FIG. 6 illustrates an embodiment of a process for maintaining a distancemetric across correlithm objects;

FIG. 7 illustrates an embodiment of a correlithm object processingsystem that performs error detection and correction;

FIG. 8 illustrates an embodiment of a correlithm object processingsystem that performs error correction using demultiplexers andmultiplexers;

FIG. 9 illustrates an embodiment of a correlithm object processingsystem that implements transparency and traceability;

FIG. 10 illustrates an embodiment of a correlithm object processingsystem that implements coding;

FIGS. 11A and 11B illustrate an embodiment of distance tables used bythe correlithm object processing system of FIG. 10; and

FIG. 12 illustrates an embodiment of a correlithm object processingsystem that uses mobile correlithm object devices.

DETAILED DESCRIPTION

FIGS. 1-5 describe various embodiments of how a correlithm objectprocessing system may be implemented or emulated in hardware, such as aspecial purpose computer. FIGS. 6-12 describe different embodiments ofcorrelithm object processing systems and methods to achieve numeroustechnical advantages.

FIG. 1 is a schematic view of an embodiment of a user device 100implementing correlithm objects 104 in an n-dimensional space 102.Examples of user devices 100 include, but are not limited to, desktopcomputers, mobile phones, tablet computers, laptop computers, or otherspecial purpose computer platform. The user device 100 is configured toimplement or emulate a correlithm object processing system that usescategorical numbers to represent data samples as correlithm objects 104in a high-dimensional space 102, for example a high-dimensional binarycube. Additional information about the correlithm object processingsystem is described in FIG. 3. Additional information about configuringthe user device 100 to implement or emulate a correlithm objectprocessing system is described in FIG. 5.

Conventional computers rely on the numerical order of ordinal binaryintegers representing data to perform various operations such ascounting, sorting, indexing, and mathematical calculations. Even whenperforming operations that involve other number systems (e.g. floatingpoint), conventional computers still resort to using ordinal binaryintegers to perform any operations. Ordinal based number systems onlyprovide information about the sequence order of the numbers themselvesbased on their numeric values. Ordinal numbers do not provide anyinformation about any other types of relationships for the data beingrepresented by the numeric values, such as similarity. For example, whena conventional computer uses ordinal numbers to represent data samples(e.g. images or audio signals), different data samples are representedby different numeric values. The different numeric values do not provideany information about how similar or dissimilar one data sample is fromanother. In other words, conventional computers are only able to makebinary comparisons of data samples which only results in determiningwhether the data samples match or do not match. Unless there is an exactmatch in ordinal number values, conventional systems are unable to tellif a data sample matches or is similar to any other data samples. As aresult, conventional computers are unable to use ordinal numbers bythemselves for determining similarity between different data samples,and instead these computers rely on complex signal processingtechniques. Determining whether a data sample matches or is similar toother data samples is not a trivial task and poses several technicalchallenges for conventional computers. These technical challenges resultin complex processes that consume processing power which reduces thespeed and performance of the system.

In contrast to conventional systems, the user device 100 operates as aspecial purpose machine for implementing or emulating a correlithmobject processing system. Implementing or emulating a correlithm objectprocessing system improves the operation of the user device 100 byenabling the user device 100 to perform non-binary comparisons (i.e.match or no match) between different data samples. This enables the userdevice 100 to quantify a degree of similarity between different datasamples. This increases the flexibility of the user device 100 to workwith data samples having different data types and/or formats, and alsoincreases the speed and performance of the user device 100 whenperforming operations using data samples. These improvements and otherbenefits to the user device 100 are described in more detail below andthroughout the disclosure.

For example, the user device 100 employs the correlithm objectprocessing system to allow the user device 100 to compare data sampleseven when the input data sample does not exactly match any known orpreviously stored input values. Implementing a correlithm objectprocessing system fundamentally changes the user device 100 and thetraditional data processing paradigm. Implementing the correlithm objectprocessing system improves the operation of the user device 100 byenabling the user device 100 to perform non-binary comparisons of datasamples. In other words, the user device 100 is able to determine howsimilar the data samples are to each other even when the data samplesare not exact matches. In addition, the user device 100 is able toquantify how similar data samples are to one another. The ability todetermine how similar data samples are to each other is unique anddistinct from conventional computers that can only perform binarycomparisons to identify exact matches.

The user device's 100 ability to perform non-binary comparisons of datasamples also fundamentally changes traditional data searching paradigms.For example, conventional search engines rely on finding exact matchesor exact partial matches of search tokens to identify related datasamples. For instance, conventional text-based search engines arelimited to finding related data samples that have text that exactlymatches other data samples. These search engines only provide a binaryresult that identifies whether or not an exact match was found based onthe search token. Implementing the correlithm object processing systemimproves the operation of the user device 100 by enabling the userdevice 100 to identify related data samples based on how similar thesearch token is to other data sample. These improvements result inincreased flexibility and faster search time when using a correlithmobject processing system. The ability to identify similarities betweendata samples expands the capabilities of a search engine to include datasamples that may not have an exact match with a search token but arestill related and similar in some aspects. The user device 100 is alsoable to quantify how similar data samples are to each other based oncharacteristics besides exact matches to the search token. Implementingthe correlithm object processing system involves operating the userdevice 100 in an unconventional manner to achieve these technologicalimprovements as well as other benefits described below for the userdevice 100.

Computing devices typically rely on the ability to compare data sets(e.g. data samples) to one another for processing. For example, insecurity or authentication applications a computing device is configuredto compare an input of an unknown person to a data set of known people(or biometric information associated with these people). The problemsassociated with comparing data sets and identifying matches based on thecomparison are problems necessarily rooted in computer technologies. Asdescribed above, conventional systems are limited to a binary comparisonthat can only determine whether an exact match is found. As an example,an input data sample that is an image of a person may have differentlighting conditions than previously stored images. In this example,different lighting conditions can make images of the same person appeardifferent from each other. Conventional computers are unable todistinguish between two images of the same person with differentlighting conditions and two images of two different people withoutcomplicated signal processing. In both of these cases, conventionalcomputers can only determine that the images are different. This isbecause conventional computers rely on manipulating ordinal numbers forprocessing.

In contrast, the user device 100 uses an unconventional configurationthat uses correlithm objects to represent data samples. Using correlithmobjects to represent data samples fundamentally changes the operation ofthe user device 100 and how the device views data samples. Byimplementing a correlithm object processing system, the user device 100can determine the distance between the data samples and other known datasamples to determine whether the input data sample matches or is similarto the other known data samples, as explained in detail below. Unlikethe conventional computers described in the previous example, the userdevice 100 is able to distinguish between two images of the same personwith different lighting conditions and two images of two differentpeople by using correlithm objects 104. Correlithm objects allow theuser device 100 to determine whether there are any similarities betweendata samples, such as between two images that are different from eachother in some respects but similar in other respects. For example, theuser device 100 is able to determine that despite different lightingconditions, the same person is present in both images.

In addition, the user device 100 is able to determine a degree ofsimilarity that quantifies how similar different data samples are to oneanother. Implementing a correlithm object processing system in the userdevice 100 improves the operation of the user device 100 when comparingdata sets and identifying matches by allowing the user device 100 toperform non-binary comparisons between data sets and to quantify thesimilarity between different data samples. In addition, using acorrelithm object processing system results in increased flexibility andfaster search times when comparing data samples or data sets. Thus,implementing a correlithm object processing system in the user device100 provides a technical solution to a problem necessarily rooted incomputer technologies.

The ability to implement a correlithm object processing system providesa technical advantage by allowing the system to identify and comparedata samples regardless of whether an exact match has been previousobserved or stored. In other words, using the correlithm objectprocessing system the user device 100 is able to identify similar datasamples to an input data sample in the absence of an exact match. Thisfunctionality is unique and distinct from conventional computers thatcan only identify data samples with exact matches.

Examples of data samples include, but are not limited to, images, files,text, audio signals, biometric signals, electric signals, or any othersuitable type of data. A correlithm object 104 is a point in then-dimensional space 102, sometimes called an “n-space.” The value ofrepresents the number of dimensions of the space. For example, ann-dimensional space 102 may be a 3-dimensional space, a 50-dimensionalspace, a 100-dimensional space, or any other suitable dimension space.The number of dimensions depends on its ability to support certainstatistical tests, such as the distances between pairs of randomlychosen points in the space approximating a normal distribution. In someembodiments, increasing the number of dimensions in the n-dimensionalspace 102 modifies the statistical properties of the system to provideimproved results. Increasing the number of dimensions increases theprobability that a correlithm object 104 is similar to other adjacentcorrelithm objects 104. In other words, increasing the number ofdimensions increases the correlation between how close a pair ofcorrelithm objects 104 are to each other and how similar the correlithmobjects 104 are to each other.

Correlithm object processing systems use new types of data structurescalled correlithm objects 104 that improve the way a device operates,for example, by enabling the device to perform non-binary data setcomparisons and to quantify the similarity between different datasamples. Correlithm objects 104 are data structures designed to improvethe way a device stores, retrieves, and compares data samples in memory.Unlike conventional data structures, correlithm objects 104 are datastructures where objects can be expressed in a high-dimensional spacesuch that distance 106 between points in the space represent thesimilarity between different objects or data samples. In other words,the distance 106 between a pair of correlithm objects 104 in then-dimensional space 102 indicates how similar the correlithm objects 104are from each other and the data samples they represent. Correlithmobjects 104 that are close to each other are more similar to each otherthan correlithm objects 104 that are further apart from each other. Forexample, in a facial recognition application, correlithm objects 104used to represent images of different types of glasses may be relativelyclose to each other compared to correlithm objects 104 used to representimages of other features such as facial hair. An exact match between twodata samples occurs when their corresponding correlithm objects 104 arethe same or have no distance between them. When two data samples are notexact matches but are similar, the distance between their correlithmobjects 104 can be used to indicate their similarities. In other words,the distance 106 between correlithm objects 104 can be used to identifyboth data samples that exactly match each other as well as data samplesthat do not match but are similar. This feature is unique to acorrelithm processing system and is unlike conventional computers thatare unable to detect when data samples are different but similar in someaspects.

Correlithm objects 104 also provide a data structure that is independentof the data type and format of the data samples they represent.Correlithm objects 104 allow data samples to be directly comparedregardless of their original data type and/or format. In some instances,comparing data samples as correlithm objects 104 is computationally moreefficient and faster than comparing data samples in their originalformat. For example, comparing images using conventional data structuresinvolves significant amounts of image processing which is time consumingand consumes processing resources. Thus, using correlithm objects 104 torepresent data samples provides increased flexibility and improvedperformance compared to using other conventional data structures.

In one embodiment, correlithm objects 104 may be represented usingcategorical binary strings. The number of bits used to represent thecorrelithm object 104 corresponds with the number of dimensions of then-dimensional space 102 where the correlithm object 102 is located. Forexample, each correlithm object 104 may be uniquely identified using a64-bit string in a 64-dimensional space 102. As another example, eachcorrelithm object 104 may be uniquely identified using a 10-bit stringin a 10-dimensional space 102. In other examples, correlithm objects 104can be identified using any other suitable number of bits in a stringthat corresponds with the number of dimensions in the n-dimensionalspace 102.

In this configuration, the distance 106 between two correlithm objects104 can be determined based on the differences between the bits of thetwo correlithm objects 104. In other words, the distance 106 between twocorrelithm objects can be determined based on how many individual bitsdiffer between the correlithm objects 104. The distance 106 between twocorrelithm objects 104 can be computed using Hamming distance or anyother suitable technique.

As an example using a 10-dimensional space 102, a first correlithmobject 104 is represented by a first 10-bit string (1001011011) and asecond correlithm object 104 is represented by a second 10-bit string(1000011011). The Hamming distance corresponds with the number of bitsthat differ between the first correlithm object 104 and the secondcorrelithm object 104. In other words, the Hamming distance between thefirst correlithm object 104 and the second correlithm object 104 can becomputed as follows:

$\frac{\begin{matrix}1001011011 \\1000011011\end{matrix}}{0001000000}$

In this example, the Hamming distance is equal to one because only onebit differs between the first correlithm object 104 and the secondcorrelithm object. As another example, a third correlithm object 104 isrepresented by a third 10-bit string (0110100100). In this example, theHamming distance between the first correlithm object 104 and the thirdcorrelithm object 104 can be computed as follows:

$\frac{\begin{matrix}1001011011 \\0110100100\end{matrix}}{1111111111}$

The Hamming distance is equal to ten because all of the bits aredifferent between the first correlithm object 104 and the thirdcorrelithm object 104. In the previous example, a Hamming distance equalto one indicates that the first correlithm object 104 and the secondcorrelithm object 104 are close to each other in the n-dimensional space102, which means they are similar to each other. In the second example,a Hamming distance equal to ten indicates that the first correlithmobject 104 and the third correlithm object 104 are further from eachother in the n-dimensional space 102 and are less similar to each otherthan the first correlithm object 104 and the second correlithm object104. In other words, the similarity between a pair of correlithm objectscan be readily determined based on the distance between the paircorrelithm objects.

As another example, the distance between a pair of correlithm objects104 can be determined by performing an XOR operation between the pair ofcorrelithm objects 104 and counting the number of logical high values inthe binary string. The number of logical high values indicates thenumber of bits that are different between the pair of correlithm objects104 which also corresponds with the Hamming distance between the pair ofcorrelithm objects 104.

In another embodiment, the distance 106 between two correlithm objects104 can be determined using a Minkowski distance such as the Euclideanor “straight-line” distance between the correlithm objects 104. Forexample, the distance 106 between a pair of correlithm objects 104 maybe determined by calculating the square root of the sum of squares ofthe coordinate difference in each dimension.

The user device 100 is configured to implement or emulate a correlithmobject processing system that comprises one or more sensors 302, nodes304, and/or actors 306 in order to convert data samples betweenreal-world values or representations and to correlithm objects 104 in acorrelithm object domain. Sensors 302 are generally configured toconvert real-world data samples to the correlithm object domain. Nodes304 are generally configured to process or perform various operations oncorrelithm objects in the correlithm object domain. Actors 306 aregenerally configured to convert correlithm objects 104 into real-worldvalues or representations. Additional information about sensors 302,nodes 304, and actors 306 is described in FIG. 3.

Performing operations using correlithm objects 104 in a correlithmobject domain allows the user device 100 to identify relationshipsbetween data samples that cannot be identified using conventional dataprocessing systems. For example, in the correlithm object domain, theuser device 100 is able to identify not only data samples that exactlymatch an input data sample, but also other data samples that havesimilar characteristics or features as the input data samples.Conventional computers are unable to identify these types ofrelationships readily. Using correlithm objects 104 improves theoperation of the user device 100 by enabling the user device 100 toefficiently process data samples and identify relationships between datasamples without relying on signal processing techniques that require asignificant amount of processing resources. These benefits allow theuser device 100 to operate more efficiently than conventional computersby reducing the amount of processing power and resources that are neededto perform various operations.

FIG. 2 is a schematic view of an embodiment of a mapping betweencorrelithm objects 104 in different n-dimensional spaces 102. Whenimplementing a correlithm object processing system, the user device 100performs operations within the correlithm object domain using correlithmobjects 104 in different n-dimensional spaces 102. As an example, theuser device 100 may convert different types of data samples havingreal-world values into correlithm objects 104 in different n-dimensionalspaces 102. For instance, the user device 100 may convert data samplesof text into a first set of correlithm objects 104 in a firstn-dimensional space 102 and data samples of audio samples as a secondset of correlithm objects 104 in a second n-dimensional space 102.Conventional systems require data samples to be of the same type and/orformat in order to perform any kind of operation on the data samples. Insome instances, some types of data samples cannot be compared becausethere is no common format available. For example, conventional computersare unable to compare data samples of images and data samples of audiosamples because there is no common format. In contrast, the user device100 implementing a correlithm object processing system is able tocompare and perform operations using correlithm objects 104 in thecorrelithm object domain regardless of the type or format of theoriginal data samples.

In FIG. 2, a first set of correlithm objects 104A are defined within afirst n-dimensional space 102A and a second set of correlithm objects104B are defined within a second n-dimensional space 102B. Then-dimensional spaces may have the same number of dimensions or adifferent number of dimensions. For example, the first n-dimensionalspace 102A and the second n-dimensional space 102B may both be threedimensional spaces. As another example, the first n-dimensional space102A may be a three dimensional space and the second n-dimensional space102B may be a nine dimensional space. Correlithm objects 104 in thefirst n-dimensional space 102A and second n-dimensional space 102B aremapped to each other. In other words, a correlithm object 104A in thefirst n-dimensional space 102A may reference or be linked with aparticular correlithm object 104B in the second n-dimensional space102B. The correlithm objects 104 may also be linked with and referencedwith other correlithm objects 104 in other n-dimensional spaces 102.

In one embodiment, a data structure such as table 200 may be used to mapor link correlithm objects 104 in different n-dimensional spaces 102. Insome instances, table 200 is referred to as a node table. Table 200 isgenerally configured to identify a first plurality of correlithm objects104 in a first n-dimensional space 102 and a second plurality ofcorrelithm objects 104 in a second n-dimensional space 102. Eachcorrelithm object 104 in the first n-dimensional space 102 is linkedwith a correlithm object 104 is the second n-dimensional space 102. Forexample, table 200 may be configured with a first column 202 that listscorrelithm objects 104A as source correlithm objects and a second column204 that lists corresponding correlithm objects 104B as targetcorrelithm objects. In other examples, table 200 may be configured inany other suitable manner or may be implemented using any other suitabledata structure. In some embodiments, one or more mapping functions maybe used to convert between a correlithm object 104 in a firstn-dimensional space and a correlithm object 104 is a secondn-dimensional space.

FIG. 3 is a schematic view of an embodiment of a correlithm objectprocessing system 300 that is implemented by a user device 100 toperform operations using correlithm objects 104. The system 300generally comprises a sensor 302, a node 304, and an actor 306. Thesystem 300 may be configured with any suitable number and/orconfiguration of sensors 302, nodes 304, and actors 306. An example ofthe system 300 in operation is described in FIG. 4. In one embodiment, asensor 302, a node 304, and an actor 306 may all be implemented on thesame device (e.g. user device 100). In other embodiments, a sensor 302,a node 304, and an actor 306 may each be implemented on differentdevices in signal communication with each other for example over anetwork. In other embodiments, different devices may be configured toimplement any combination of sensors 302, nodes 304, and actors 306.

Sensors 302 serve as interfaces that allow a user device 100 to convertreal-world data samples into correlithm objects 104 that can be used inthe correlithm object domain. Sensors 302 enable the user device 100 tocompare and perform operations using correlithm objects 104 regardlessof the data type or format of the original data sample. Sensors 302 areconfigured to receive a real-world value 320 representing a data sampleas an input, to determine a correlithm object 104 based on thereal-world value 320, and to output the correlithm object 104. Forexample, the sensor 302 may receive an image 301 of a person and outputa correlithm object 322 to the node 304 or actor 306. In one embodiment,sensors 302 are configured to use sensor tables 308 that link aplurality of real-world values with a plurality of correlithm objects104 in an n-dimensional space 102. Real-world values are any type ofsignal, value, or representation of data samples. Examples of real-worldvalues include, but are not limited to, images, pixel values, text,audio signals, electrical signals, and biometric signals. As an example,a sensor table 308 may be configured with a first column 312 that listsreal-world value entries corresponding with different images and asecond column 314 that lists corresponding correlithm objects 104 asinput correlithm objects. In other examples, sensor tables 308 may beconfigured in any other suitable manner or may be implemented using anyother suitable data structure. In some embodiments, one or more mappingfunctions may be used to translate between a real-world value 320 and acorrelithm object 104 in an n-dimensional space. Additional informationfor implementing or emulating a sensor 302 in hardware is described inFIG. 5.

Nodes 304 are configured to receive a correlithm object 104 (e.g. aninput correlithm object 104), to determine another correlithm object 104based on the received correlithm object 104, and to output theidentified correlithm object 104 (e.g. an output correlithm object 104).In one embodiment, nodes 304 are configured to use node tables 200 thatlink a plurality of correlithm objects 104 from a first n-dimensionalspace 102 with a plurality of correlithm objects 104 in a secondn-dimensional space 102. A node table 200 may be configured similar tothe table 200 described in FIG. 2. Additional information forimplementing or emulating a node 304 in hardware is described in FIG. 5.

Actors 306 serve as interfaces that allow a user device 100 to convertcorrelithm objects 104 in the correlithm object domain back toreal-world values or data samples. Actors 306 enable the user device 100to convert from correlithm objects 104 into any suitable type ofreal-world value. Actors 306 are configured to receive a correlithmobject 104 (e.g. an output correlithm object 104), to determine areal-world output value 326 based on the received correlithm object 104,and to output the real-world output value 326. The real-world outputvalue 326 may be a different data type or representation of the originaldata sample. As an example, the real-world input value 320 may be animage 301 of a person and the resulting real-world output value 326 maybe text 327 and/or an audio signal identifying the person. In oneembodiment, actors 306 are configured to use actor tables 310 that linka plurality of correlithm objects 104 in an n-dimensional space 102 witha plurality of real-world values. As an example, an actor table 310 maybe configured with a first column 316 that lists correlithm objects 104as output correlithm objects and a second column 318 that listsreal-world values. In other examples, actor tables 310 may be configuredin any other suitable manner or may be implemented using any othersuitable data structure. In some embodiments, one or more mappingfunctions may be employed to translate between a correlithm object 104in an n-dimensional space and a real-world output value 326. Additionalinformation for implementing or emulating an actor 306 in hardware isdescribed in FIG. 5.

A correlithm object processing system 300 uses a combination of a sensortable 308, a node table 200, and/or an actor table 310 to provide aspecific set of rules that improve computer-related technologies byenabling devices to compare and to determine the degree of similaritybetween different data samples regardless of the data type and/or formatof the data sample they represent. The ability to directly compare datasamples having different data types and/or formatting is a newfunctionality that cannot be performed using conventional computingsystems and data structures. Conventional systems require data samplesto be of the same type and/or format in order to perform any kind ofoperation on the data samples. In some instances, some types of datasamples are incompatible with each other and cannot be compared becausethere is no common format available. For example, conventional computersare unable to compare data samples of images with data samples of audiosamples because there is no common format available. In contrast, adevice implementing a correlithm object processing system uses acombination of a sensor table 308, a node table 200, and/or an actortable 310 to compare and perform operations using correlithm objects 104in the correlithm object domain regardless of the type or format of theoriginal data samples. The correlithm object processing system 300 usesa combination of a sensor table 308, a node table 200, and/or an actortable 310 as a specific set of rules that provides a particular solutionto dealing with different types of data samples and allows devices toperform operations on different types of data samples using correlithmobjects 104 in the correlithm object domain. In some instances,comparing data samples as correlithm objects 104 is computationally moreefficient and faster than comparing data samples in their originalformat. Thus, using correlithm objects 104 to represent data samplesprovides increased flexibility and improved performance compared tousing other conventional data structures. The specific set of rules usedby the correlithm object processing system 300 go beyond simply usingroutine and conventional activities in order to achieve this newfunctionality and performance improvements.

In addition, correlithm object processing system 300 uses a combinationof a sensor table 308, a node table 200, and/or an actor table 310 toprovide a particular manner for transforming data samples betweenordinal number representations and correlithm objects 104 in acorrelithm object domain. For example, the correlithm object processingsystem 300 may be configured to transform a representation of a datasample into a correlithm object 104, to perform various operations usingthe correlithm object 104 in the correlithm object domain, and totransform a resulting correlithm object 104 into another representationof a data sample. Transforming data samples between ordinal numberrepresentations and correlithm objects 104 involves fundamentallychanging the data type of data samples between an ordinal number systemand a categorical number system to achieve the previously describedbenefits of the correlithm object processing system 300.

FIG. 4 is a protocol diagram of an embodiment of a correlithm objectprocess flow 400. A user device 100 implements process flow 400 toemulate a correlithm object processing system 300 to perform operationsusing correlithm object 104 such as facial recognition. The user device100 implements process flow 400 to compare different data samples (e.g.images, voice signals, or text) to each other and to identify otherobjects based on the comparison. Process flow 400 provides instructionsthat allows user devices 100 to achieve the improved technical benefitsof a correlithm object processing system 300.

Conventional systems are configured to use ordinal numbers foridentifying different data samples. Ordinal based number systems onlyprovide information about the sequence order of numbers based on theirnumeric values, and do not provide any information about any other typesof relationships for the data samples being represented by the numericvalues such as similarity. In contrast, a user device 100 can implementor emulate the correlithm object processing system 300 which provides anunconventional solution that uses categorical numbers and correlithmobjects 104 to represent data samples. For example, the system 300 maybe configured to use binary integers as categorical numbers to generatecorrelithm objects 104 which enables the user device 100 to performoperations directly based on similarities between different datasamples. Categorical numbers provide information about how similardifferent data sample are from each other. Correlithm objects 104generated using categorical numbers can be used directly by the system300 for determining how similar different data samples are from eachother without relying on exact matches, having a common data type orformat, or conventional signal processing techniques.

A non-limiting example is provided to illustrate how the user device 100implements process flow 400 to emulate a correlithm object processingsystem 300 to perform facial recognition on an image to determine theidentity of the person in the image. In other examples, the user device100 may implement process flow 400 to emulate a correlithm objectprocessing system 300 to perform voice recognition, text recognition, orany other operation that compares different objects.

At step 402, a sensor 302 receives an input signal representing a datasample. For example, the sensor 302 receives an image of person's faceas a real-world input value 320. The input signal may be in any suitabledata type or format. In one embodiment, the sensor 302 may obtain theinput signal in real-time from a peripheral device (e.g. a camera). Inanother embodiment, the sensor 302 may obtain the input signal from amemory or database.

At step 404, the sensor 302 identifies a real-world value entry in asensor table 308 based on the input signal. In one embodiment, thesystem 300 identifies a real-world value entry in the sensor table 308that matches the input signal. For example, the real-world value entriesmay comprise previously stored images. The sensor 302 may compare thereceived image to the previously stored images to identify a real-worldvalue entry that matches the received image. In one embodiment, when thesensor 302 does not find an exact match, the sensor 302 finds areal-world value entry that closest matches the received image.

At step 406, the sensor 302 identifies and fetches an input correlithmobject 104 in the sensor table 308 linked with the real-world valueentry. At step 408, the sensor 302 sends the identified input correlithmobject 104 to the node 304. In one embodiment, the identified inputcorrelithm object 104 is represented in the sensor table 308 using acategorical binary integer string. The sensor 302 sends the binarystring representing to the identified input correlithm object 104 to thenode 304.

At step 410, the node 304 receives the input correlithm object 104 anddetermines distances 106 between the input correlithm object 104 andeach source correlithm object 104 in a node table 200. In oneembodiment, the distance 106 between two correlithm objects 104 can bedetermined based on the differences between the bits of the twocorrelithm objects 104. In other words, the distance 106 between twocorrelithm objects can be determined based on how many individual bitsdiffer between a pair of correlithm objects 104. The distance 106between two correlithm objects 104 can be computed using Hammingdistance or any other suitable technique. In another embodiment, thedistance 106 between two correlithm objects 104 can be determined usinga Minkowski distance such as the Euclidean or “straight-line” distancebetween the correlithm objects 104. For example, the distance 106between a pair of correlithm objects 104 may be determined bycalculating the square root of the sum of squares of the coordinatedifference in each dimension.

At step 412, the node 304 identifies a source correlithm object 104 fromthe node table 200 with the shortest distance 106. A source correlithmobject 104 with the shortest distance from the input correlithm object104 is a correlithm object 104 either matches or most closely matchesthe received input correlithm object 104.

At step 414, the node 304 identifies and fetches a target correlithmobject 104 in the node table 200 linked with the source correlithmobject 104. At step 416, the node 304 outputs the identified targetcorrelithm object 104 to the actor 306. In this example, the identifiedtarget correlithm object 104 is represented in the node table 200 usinga categorical binary integer string. The node 304 sends the binarystring representing to the identified target correlithm object 104 tothe actor 306.

At step 418, the actor 306 receives the target correlithm object 104 anddetermines distances between the target correlithm object 104 and eachoutput correlithm object 104 in an actor table 310. The actor 306 maycompute the distances between the target correlithm object 104 and eachoutput correlithm object 104 in an actor table 310 using a processsimilar to the process described in step 410.

At step 420, the actor 306 identifies an output correlithm object 104from the actor table 310 with the shortest distance 106. An outputcorrelithm object 104 with the shortest distance from the targetcorrelithm object 104 is a correlithm object 104 either matches or mostclosely matches the received target correlithm object 104.

At step 422, the actor 306 identifies and fetches a real-world outputvalue in the actor table 310 linked with the output correlithm object104. The real-world output value may be any suitable type of data samplethat corresponds with the original input signal. For example, thereal-world output value may be text that indicates the name of theperson in the image or some other identifier associated with the personin the image. As another example, the real-world output value may be anaudio signal or sample of the name of the person in the image. In otherexamples, the real-world output value may be any other suitablereal-world signal or value that corresponds with the original inputsignal. The real-world output value may be in any suitable data type orformat.

At step 424, the actor 306 outputs the identified real-world outputvalue. In one embodiment, the actor 306 may output the real-world outputvalue in real-time to a peripheral device (e.g. a display or a speaker).In one embodiment, the actor 306 may output the real-world output valueto a memory or database. In one embodiment, the real-world output valueis sent to another sensor 302. For example, the real-world output valuemay be sent to another sensor 302 as an input for another process.

FIG. 5 is a schematic diagram of an embodiment of a computerarchitecture 500 for emulating a correlithm object processing system 300in a user device 100. The computer architecture 500 comprises aprocessor 502, a memory 504, a network interface 506, and aninput-output (I/O) interface 508. The computer architecture 500 may beconfigured as shown or in any other suitable configuration.

The processor 502 comprises one or more processors operably coupled tothe memory 504. The processor 502 is any electronic circuitry including,but not limited to, state machines, one or more central processing unit(CPU) chips, logic units, cores (e.g. a multi-core processor),field-programmable gate array (FPGAs), application specific integratedcircuits (ASICs), graphics processing units (GPUs), or digital signalprocessors (DSPs). The processor 502 may be a programmable logic device,a microcontroller, a microprocessor, or any suitable combination of thepreceding. The processor 502 is communicatively coupled to and in signalcommunication with the memory 204. The one or more processors areconfigured to process data and may be implemented in hardware orsoftware. For example, the processor 502 may be 8-bit, 16-bit, 32-bit,64-bit or of any other suitable architecture. The processor 502 mayinclude an arithmetic logic unit (ALU) for performing arithmetic andlogic operations, processor registers that supply operands to the ALUand store the results of ALU operations, and a control unit that fetchesinstructions from memory and executes them by directing the coordinatedoperations of the ALU, registers and other components.

The one or more processors are configured to implement variousinstructions. For example, the one or more processors are configured toexecute instructions to implement sensor engines 510, node engines 512,and actor engines 514. In an embodiment, the sensor engines 510, thenode engines 512, and the actor engines 514 are implemented using logicunits, FPGAs, ASICs, DSPs, or any other suitable hardware. The sensorengines 510, the node engines 512, and the actor engines 514 are eachconfigured to implement a specific set of rules or process that providesan improved technological result.

In one embodiment, the sensor engine 510 is configured implement sensors302 that receive a real-world value 320 as an input, determine acorrelithm object 104 based on the real-world value 320, and output thecorrelithm object 104. An example operation of a sensor 302 implementedby a sensor engine 510 is described in FIG. 4.

In one embodiment, the node engine 512 is configured to implement nodes304 that receive a correlithm object 104 (e.g. an input correlithmobject 104), determine another correlithm object 104 based on thereceived correlithm object 104, and output the identified correlithmobject 104 (e.g. an output correlithm object 104). A node 304implemented by a node engine 512 is also configured to compute distancesbetween pairs of correlithm objects 104. An example operation of a node304 implemented by a node engine 512 is described in FIG. 4.

In one embodiment, the actor engine 514 is configured to implementactors 306 that receive a correlithm object 104 (e.g. an outputcorrelithm object 104), determine a real-world output value 326 based onthe received correlithm object 104, and output the real-world outputvalue 326. An example operation of an actor 306 implemented by an actorengine 514 is described in FIG. 4.

The memory 504 comprises one or more non-transitory disks, tape drives,or solid-state drives, and may be used as an over-flow data storagedevice, to store programs when such programs are selected for execution,and to store instructions and data that are read during programexecution. The memory 504 may be volatile or non-volatile and maycomprise read-only memory (ROM), random-access memory (RAM), ternarycontent-addressable memory (TCAM), dynamic random-access memory (DRAM),and static random-access memory (SRAM). The memory 504 is operable tostore sensor instructions 516, node instructions 518, actor instructions520, sensor tables 308, node tables 200, actor tables 310, referencetables 704, sensor output tables 910, node output tables 920, actoroutput tables 930, distance tables 1008 and 1012, and/or any other dataor instructions. The sensor instructions 516, the node instructions 518,and the actor instructions 520 comprise any suitable set ofinstructions, logic, rules, or code operable to execute the sensorengine 510, node engine 512, and the actor engine 514, respectively.

The sensor tables 308, the node tables 200, and the actor tables 310 maybe configured similar to the sensor tables 308, the node tables 200, andthe actor tables 310 described in FIG. 3, respectively.

The network interface 506 is configured to enable wired and/or wirelesscommunications. The network interface 506 is configured to communicatedata with any other device or system. For example, the network interface506 may be configured for communication with a modem, a switch, arouter, a bridge, a server, or a client. The processor 502 is configuredto send and receive data using the network interface 506.

The I/O interface 508 may comprise ports, transmitters, receivers,transceivers, or any other devices for transmitting and/or receivingdata with peripheral devices as would be appreciated by one of ordinaryskill in the art upon viewing this disclosure. For example, the I/Ointerface 508 may be configured to communicate data between theprocessor 502 and peripheral hardware such as a graphical userinterface, a display, a mouse, a keyboard, a key pad, and a touch sensor(e.g. a touch screen).

FIG. 6 illustrates one embodiment of a correlithm object process flow600. A user device 100 implements process flow 600 to emulate acorrelithm object processing system 300 to perform operations usingcorrelithm objects 104. The user device 100 implements process flow 600to compare different data samples (e.g. images, voice signals, or text)to each other and to identify other objects based on the comparison.Process flow 600 provides instructions that allows user devices 100 toachieve the improved technical benefits of a correlithm objectprocessing system 300.

An example is provided to illustrate how the user device 100 implementsprocess flow 600 to emulate a correlithm object processing system 300 toperform facial recognition on an image to determine the identity of theperson in the image. In other examples, the user device 100 mayimplement process flow 600 to emulate a correlithm object processingsystem 300 to perform voice recognition, text recognition, or any otheroperation that compares different data.

At step 602, the memory 504 stores a node table 200 that links sourcecorrelithm objects 104 with target correlithm objects 104, asillustrated in FIGS. 2 and 3. At step 604, node 304 receives an inputcorrelithm object 104 and at step 606 the node 304 determines distances106 between the input correlithm object 104 and each source correlithmobject 104 in node table 200. In one embodiment, the distance 106between two correlithm objects 104 can be determined based on thedifferences between the bits of the two correlithm objects 104. In otherwords, the distance 106 between two correlithm objects can be determinedbased on how many individual bits differ between a pair of correlithmobjects 104. The distance 106 between two correlithm objects 104 can becomputed using Hamming distance, anti-Hamming distance, or any othersuitable technique. In another embodiment, the distance 106 between twocorrelithm objects 104 can be determined using a Minkowski distance suchas the Euclidean or “straight-line” distance between the correlithmobjects 104. For example, the distance 106 between a pair of correlithmobjects 104 may be determined by calculating the square root of the sumof squares of the coordinate difference in each dimension.

At step 608, node 304 identifies a source correlithm object 104 fromnode table 200 with the shortest distance 106. A source correlithmobject 104 with the shortest distance from the input correlithm object104 is a correlithm object 104 that either matches or most closelymatches the received input correlithm object 104 from among all of thesource correlithm objects 104 in node table 200.

At step 610, the node 304 identifies a first target correlithm object104 in the node table 200 linked with the source correlithm object 104identified at step 608. In some embodiments, it may be technicallyadvantageous to output the first target correlithm object 104 identifiedat step 610 for use by the rest of the correlithm object processingsystem 300 despite the fact that input correlithm object 104 and thesource correlithm object 104 identified at step 608 are not exactmatches. For example, in a system where the input correlithm object 104becomes altered during communication, or is modified in some other wayduring processing, then it may be advantageous from an error correctionstandpoint to find the closest match to input correlithm object 104among source correlithm objects 104 in node table and proceed with thecorresponding target correlithm object 104. This approach is describedwith reference to FIG. 4. In other embodiments, however, it may beadvantageous to propagate any alterations experienced by inputcorrelithm object 104 or any differences between input correlithm object104 and the source correlithm object 104 identified at step 608 to thetarget correlithm object 104 that is output by node 304. This approachis described herein with respect to FIG. 6.

At step 612, node 304 generates a second target correlithm object 104that is offset from the first target correlithm object 104 by thedistance in n-dimensional space between the input correlithm object 104received at step 604 and the source correlithm object 104 identified atstep 608. For example, assume that input correlithm object 104, sourcecorrelithm objects 104 and target correlithm objects 104 are each 64-bitbinary strings. Further assume that the Hamming distance between inputcorrelithm object 104 received at step 604 and source correlithm object104 identified at step 608 was determined to be four. In this example,node 304 may generate a second target correlithm object 104 at step 612that differs from the first target correlithm object 104 identified atstep 610 by four bits. Thus, second target correlithm object 104generated at step 612 will be the same n-dimensional distance away fromfirst target correlithm object 104 identified at step 610 as then-dimensional distance between the input correlithm object 104 receivedat step 604 and the source correlithm object 104 identified at step 608.In this way, correlithm object processing system 300 can propagate anyerrors or modifications to correlithm objects 104 as they are processedwhile still gaining the benefits of using correlithm objects torepresent data.

In another example, assume that input correlithm object 104 and sourcecorrelithm objects 104 are each 64-bit binary strings and targetcorrelithm objects 104 are each 128-bit binary strings (i.e., theyrepresent data in different n-dimensional spaces). Further assume thatthe Hamming distance between input correlithm object 104 received atstep 604 and source correlithm object 104 identified at step 608 wasdetermined to be four. In this example, node 304 may still generate asecond target correlithm object 104 at step 612 that differs from thefirst target correlithm object 104 identified at step 610 by four bits.Thus, second target correlithm object 104 generated at step 612 will bethe same n-dimensional distance away from first target correlithm object104 identified at step 610 as the n-dimensional distance between theinput correlithm object 104 received at step 604 and the sourcecorrelithm object 104 identified at step 608 despite residing indifferent n-dimensional spaces. In this way, correlithm objectprocessing system 300 can propagate any errors or modifications tocorrelithm objects 104 as they are processed while still gaining thebenefits of using correlithm objects to represent data in differentn-dimensional spaces.

In still another example, assume that input correlithm object 104 andsource correlithm objects 104 are each 64-bit binary strings and targetcorrelithm objects 104 are each 128-bit binary strings (i.e., theyrepresent data in different n-dimensional spaces). Further assume thatthe Hamming distance between input correlithm object 104 received atstep 604 and source correlithm object 104 identified at step 608 wasdetermined to be four. In this example, node 304 may generate a secondtarget correlithm object 104 at step 612 that differs from the firsttarget correlithm object 104 identified at step 610 by eight bits. Thus,second target correlithm object 104 generated at step 612 will beproportionally the same n-dimensional distance away from first targetcorrelithm object 104 identified at step 610 (in a 128-bit space) as then-dimensional distance between the input correlithm object 104 receivedat step 604 and the source correlithm object 104 identified at step 608(in a 64-bit space) despite residing in different n-dimensional spaces.In this way, correlithm object processing system 300 can propagate anyerrors or modifications to correlithm objects 104 as they are processedwhile still gaining the benefits of using correlithm objects torepresent data in different n-dimensional spaces.

At step 614, node 304 outputs the second target correlithm object 104generated at step 612. For example, node 304 may output the secondcorrelithm object 104 to the actor 306 illustrated in FIG. 4. In thisway, some or all of steps 410 through 416 illustrated in FIG. 4 may besubstituted by steps 602 through 614 of FIG. 6 in the operation of acorrelithm object processing system 300.

FIG. 7 is a schematic view of an embodiment of a correlithm objectprocessing system 700 that is implemented by one or more user devices100 to perform operations using correlithm objects 104. In oneembodiment, system 700 is a part of system 300 illustrated in FIG. 3 andcan be implemented using the computer architecture 500 illustrated inFIG. 5. For example, system 700 and its constituent components can beimplemented by processor 502, one or more of the engines 510, 512, 514,and 522, and other elements of computer architecture 500, describedabove with respect to FIG. 5. The system 700 generally includes a firstnode 304 a communicatively coupled to a second node 304 b by acommunication channel 702. Communication channel 702 may be a wiredand/or wireless medium. For example, communication channel 702 may be asatellite link between any number and combination of transmitting earthstations, satellites, and receiving earth stations. In another example,communication channel 702 may be a telecommunications link between oneor more of a mobile device, transceiver, and base station in a cellularnetwork. In still another embodiment, communication channel 702 may be acircuit trace that provides an electrical connection between componentsof an integrated circuit chip. These examples are not meant to beexhaustive and it should be understood that communication channel 702can be any physical transmission medium between elements or componentsof a system. System 700 further includes a reference table 704 thatstores a plurality of correlithm objects 104 that may be used by andcommunicated between nodes 304. Memory 504 stores reference table 704organized in any suitable data format. In one embodiment, referencetable 704 is centrally stored and accessible by one or both nodes 304 aand 304 b. In another embodiment, one or both of nodes 304 a and 304 bstores a copy of reference table 704 locally. In still anotherembodiment, one or both of nodes 304 a and 304 b may store referencetable 704 locally and/or access it remotely.

In operation, second node 304 b communicates a particular correlithmobject 104 a to first node 304 a over communication channel 702.Correlithm object 104 a may be one of the plurality of correlithmobjects 104 stored in reference table 704. In this example, assume thatcommunication channel 702 experiences noise 706 that can degrade thequality and/or accuracy of the information being communicated such as,for example, correlithm object 104 a. Sources of noise 706 oncommunication channel 702 may include intermodulation noise, crosstalk,interference, atmospheric noise, industrial noise, solar noise, orcosmic noise, among others. For example, although second node 304 b maytransmit correlithm object 104 a over communication channel 702, due tonoise 706, first node 304 a might receive an altered version ofcorrelithm object 104, referred to as correlithm object 104 a′.Depending on the magnitude and type of noise 706, the effect oncorrelithm object 104 might range from small to large. In such asituation, second node 304 b may be able to leverage the nature ofcorrelithm objects 104 to perform error correction, as described below.

To do this, first node 304 a may compare received correlithm object 104′with the plurality of correlithm objects 104 stored in reference table704 to identify the particular correlithm object 104 that wastransmitted by second node 304 b. In particular, first node 304 maydetermine the distances in n-dimensional space between correlithm object104 a′ and each of the plurality of correlithm objects 104 stored inreference table 704. In one embodiment, these distances may bedetermined by calculating Hamming distances between correlithm object104 a′ and each of the plurality of correlithm objects 104. In anotherembodiment, these distances may be determined by calculating theanti-Hamming distances between correlithm object 104 a′ and each of theplurality of correlithm objects 104. As described above, the Hammingdistance is determined based on the number of bits that differ betweenthe binary string representing correlithm object 104 a′ and each of thebinary strings representing each of the correlithm objects 104 stored inreference table 704. The anti-Hamming distance may be determined basedon the number of bits that are the same between the binary stringrepresenting correlithm object 104 a′ and each of the binary stringsrepresenting each of the correlithm objects 104 stored in referencetable 704. In still other embodiments, the distances in n-dimensionalspace between correlithm object 104 a′ and each of the correlithmobjects 104 stored in reference table 704 may be determined using aMinkowski distance or a Euclidean distance.

Upon calculating the distances between correlithm object 104 a′ and eachof the plurality of correlithm objects 104 stored in reference table 704using one of the techniques described above, first node 304 a determineswhich calculated distance is the shortest distance. This is because thecorrelithm object 104 stored in reference table 704 having the shortestdistance between it and the correlithm object 104 a′ received by firstnode 304 a is likely to be the correlithm object 104 a that wastransmitted by second node 304 b. For example, if correlithm object 104a transmitted by second node 304 b and the correlithm object 104 a′received by first node 304 a are 64-bit binary strings and they differby only four bits (e.g., Hamming distance is four) whereas the distancein n-dimensional space between correlithm object 104 a′ and each of theother correlithm objects 104 stored in reference table 704 is somewherebetween twenty-four and forty bits (e.g., Hamming distance of betweentwenty-four and forty), then the correlithm object 104 that only had afour bit difference is most likely the unaltered version of thecorrelithm object 104 a′ received at first node 304 a. Accordingly,first node 304 a may perform error correction by outputting thecorrelithm object 104 that was determined to have the shortest distancebetween it and correlithm object 104 a′.

In a particular embodiment, if the distance between the correlithmobject 104 a′ and each of the correlithm objects 104 stored in referencetable 704 is not within a predetermined number of standard deviations inn-dimensional space, then node 304 may discard correlithm object 104 a′and, instead, output an alert reporting that correlithm object 104 a′was too corrupted to use for further processing in the system 700. Inthis embodiment, second node 304 b may be prompted to communicatecorrelithm object 104 a again, or to communicate it over a differentcommunication channel to first node 304 a. To account for the situationwhere communication channel 702 experiences too much noise causingcorrelithm object 104 a′ to be unusable, the system 700 may be modifiedas illustrated in FIG. 8, and described further below.

FIG. 8 is a schematic view of an embodiment of a correlithm objectprocessing system 800 that is implemented by one or more user devices100 to perform operations using correlithm objects 104. System 800 andits constituent components can be implemented by processor 502, one ormore of the engines 510, 512, and 514, and other elements of computerarchitecture 500, described above with respect to FIG. 5. In oneembodiment, system 800 is a part of system 300 illustrated in FIG. 3.The system 800 generally includes a first node 304 a communicativelycoupled to a second node 304 b by a plurality of communication channels702 a, 702 b, and 702 c. Although FIG. 8 illustrates three communicationchannels 702 between first node 304 a and second node 304 b, it shouldbe understood that system 800 may include any suitable number ofcommunication channels 702 greater than one. System 700 further includesdemultiplexer 802 and multiplexer 800, which can be implemented byprocessor 500 of the computer architecture 500, described above withrespect to FIG. 5. Demultiplexer 802 is a device that takes informationfrom a single input line and routes it to over several output lines.Conversely, multiplexer 800 is a device that combines informationreceived over several input lines and forwards it over a single outputline. Although the other elements of system 800 refer to thecorresponding elements of system 700, the operation of system 800 isslightly different from the operation of system 700 in that correlithmobject 104 is split into different portions that are communicated tofirst node 304 a over different communication channels 702 a, 702 b, and702 c in order to minimize the risk that any given communication channel702 is too noisy.

In operation, second node 304 b communicates a particular correlithmobject 104 a to first node 304 a via demultiplexer 802, communicationchannels 702 a, 702 b, and 702 c, and multiplexer 804. Correlithm object104 a may be one of the plurality of correlithm objects 104 stored inreference table 704.

Demultiplexer 802 receives correlithm object 104 from second node 304 band divides it into a plurality of different portions, such as firstportion 104 a, second portion 104 b, and third portion 104 b. In aparticular embodiment where correlithm object 104 is a 64-bit binarystring, for example, demultiplexer node 802 may divide correlithm object104 into a first portion 104 a that includes the first sixteen bits ofthe binary string, second portion 104 b that includes the second sixteenbits of the binary string, and third portion 104 c that includes thelast sixteen bits of the binary string. In other embodiments, thegrouping of bits from the 64-bit binary string of correlithm object 104may be different among first portion 104 a, second portion 104 b, andthird portion 104 c. In still other embodiments, the sizes of the firstportion 104 a, second portion 104 b, and third portion 104 c are notnecessarily the same as each other. For example, a larger portion of thecorrelithm object 104 may be sent over communication channels 702 thatare reported to have a lower bit error rate which a smaller portion ofthe correlithm object 104 may be sent over communication channels 702that are reported to have a higher bit error rate. Demultiplexer 802transmits first portion 104 a over communication channel 702 a, secondportion 104 b over communication channel 702 b, and third portion 104 cover communication channel 702 c.

Multiplexer 804 is communicatively coupled to demultiplexer 802 bycommunication channels 702 a, 702 b, and 702 c. Multiplexer 804 receivesfirst portion 104 a over communication channel 702 a, second portion 104b over communication channel 702 b, and third portion 104 c overcommunication channel 702 c. As with communication channel 702 describedin system 700, communication channels 702 a, 702 b, and 702 c mayexperience different levels of noise 706 a, 706 b, and 706 c,respectively, which might cause multiplexer 804 to receive alteredversions of correlithm objects 104 a, 104 b, and 104 c. Becausemultiplexer 804 combines first portion 104 a′, second portion 104 b′,and third portion 104 c′ into correlithm object 104′, the noise 706 a,706 b, and 706 c in communication channels 702 a, 702 b, and 702 c,respectively, might cause first node 304 a to receive an altered versionof correlithm object 104, referred to as correlithm object 104 a′.Depending on the magnitude and type of noise 706 a, 706 b, and 706 c,the effect on correlithm object 104 might range from small to large.

As mentioned briefly above, multiplexer 804 receives first portion 104a′, second portion 104 b′, and third portion 104 c′ and combines them toform correlithm object 104′. In general, multiplexer 804 combines thevarious bits of first portion 104 a′, second portion 104 b′, and thirdportion 104 c′ in a manner corresponding to and consistent with themanner in which demultiplexer 802 divided correlithm object 104 intofirst portion 104 a, second portion 104 b, and third portion 104 c. Forexample, multiplexer node 804 may combine the sixteen bits of firstportion 104 a′ with the sixteen bits of second portion 104 b′ and thesixteen bits of third portion 104 c′ to form a 64-bit binary string thatrepresents correlithm object 104′. Because the levels of noise 706 a,706 b, and 706 c may be different among communication channels 702 a,702 b, and 702 c, the risk of a singular noisy channel that renderscorrelithm object 104′ unusable is reduced by splitting up correlithmobject 104, communicating it to first node 304 a over multiple differentcommunication channels 702 a, 702 b, and 702 c, and then recombiningthose different portions into correlithm object 104′.

In a particular embodiment, however, it is possible that a particularcommunication 702 in system 800 is so noisy that it fails. For example,if communication channel 702 b is extremely noisy such that it fails,then multiplexer node 804 may receive first portion 104 a′ and thirdportion 104 c′, but it may not receive second portion 104 b′. In thisexample, multiplexer node 804 may randomly generate a suitable binarystring (e.g., 16-bit binary string) that may substitute for the secondportion 104 b′ that multiplexer 804 was supposed to receive overcommunication channel 702 b. In particular, multiplexer node 804 mayrandomly select 1's and 0's to create a suitable binary string that maysubstitute for the second portion 104 b′ that multiplexer 804 wassupposed to receive over communication channel 702 b. Then, multiplexer804 may combine first portion 104 a′ with the randomly created binarystring and third portion 104 c′ to generate correlithm object 104′.

As described above with respect to system 700 and elsewhere above, firstnode 304 a of system 800 compares correlithm object 104′ with each ofthe correlithm objects 104 stored in reference table 704 to determinewhich has the shortest distance between them in n-dimensional space. Thecorrelithm object 104 stored in reference table 704 having the shortestdistance between it and the correlithm object 104′ received by firstnode 304 a is likely to be the correlithm object 104 that wastransmitted by second node 304 b. Accordingly, first node 304 a mayperform error correction by outputting the correlithm object 104 thatwas determined to have the shortest distance between it and correlithmobject 104′.

FIG. 9 is a schematic view of an embodiment of a correlithm objectprocessing system 900 that is implemented by a user device 100 toperform operations using correlithm objects 104. The system 900 is avariation of the system 300 illustrated in FIG. 3 and can be implementedusing the computer architecture 500 illustrated in FIG. 5. For example,system 900 and its constituent components can be implemented byprocessor 502, one or more of the engines 510, 512, and 514, and otherelements of computer architecture 500, described above with respect toFIG. 5. As with system 300, system 900 may be configured with anysuitable number and/or configuration of sensors 302, nodes 304, andactors 306. In one embodiment, a sensor 302, a node 304, and an actor306 may all be implemented on the same device (e.g. user device 100). Inother embodiments, a sensor 302, a node 304, and an actor 306 may eachbe implemented on different devices 100 in signal communication witheach other, for example over a network. In other embodiments, differentdevices 100 may be configured to implement any combination of sensors302, nodes 304, and actors 306.

Sensors 302 serve as interfaces that allow a user device 100 to convertreal-world data samples into correlithm objects 104 that can be used inthe correlithm object domain. In general, sensors 302 are configured toreceive an input signal 902 associated with a timestamp 904 and thatincludes a real-world value 320 representing a data sample. Sensor 302is further configured to determine a correlithm object 104 based on thereal-world value 320, and to output the correlithm object 104. In oneembodiment, sensors 302 are configured to use sensor tables 308 thatlink a plurality of real-world values with a plurality of correlithmobjects 104 in an n-dimensional space 102. Real-world values are anytype of signal, value, or representation of data samples. As explainedwith respect to FIG. 3, a sensor table 308 may be configured with afirst column 312 that lists real-world value entries and a second column314 that lists corresponding correlithm objects 104 as input correlithmobjects. In other examples, sensor tables 308 may be configured in anyother suitable manner or may be implemented using any other suitabledata structure. In some embodiments, one or more mapping functions maybe used to translate between a real-world value 320 and a correlithmobject 104 in an n-dimensional space.

In operation, for example, sensor 302 may receive a first input signal902 a associated with a first timestamp 904 a where the input signal 902a includes a first real-world value 320 a. First real-world value 320 amay correspond to value 3 listed in column 312 of sensor table 308 andmap to input correlithm object 3 listed in column 314 of sensor table308. Thus, sensor 302 may communicate input correlithm object 3 as anoutput.

System 900 may further include a sensor output table 910 that isconfigured with a first column 912 that lists real-world values 320received in input signals 902, second column 914 that lists inputcorrelithm objects that are mapped to those real-world values 320 bysensor 302 using sensor table 308, and third column 916 that liststimestamps 904 associated with those real-world values 320. Thus, forexample, because first real-world value 320 a was received inconjunction with input signal 902 a and corresponded to value 3 insensor table 308, the first entry of sensor output table 910 lists value3 in column 912. Furthermore, because value 3 was mapped to inputcorrelithm object 3 in sensor table 308, the first entry of sensoroutput table 910 lists input correlithm object 3 in column 914. Finally,because input signal 902 a that contained real-world value 320 a wasassociated with timestamp 904 a, the first entry of sensor output table910 lists timestamp 904 a, represented by t₁, in column 916.

In further operation of system 900, sensor 302 may receive a secondinput signal 902 b associated with a second timestamp 904 b where thesecond input signal 902 b includes a second real-world value 320 b.Second real-world value 320 b may correspond to value 2 listed in column312 of sensor table 308 and map to input correlithm object 2 listed incolumn 314 of sensor table 308. Thus, sensor 302 may communicate inputcorrelithm object 2 as an output. Because second real-world value 320 bwas received in conjunction with second input signal 902 b andcorresponded to value 2 in sensor table 308, the second entry of sensoroutput table 910 lists value 2 in column 912. Furthermore, because value2 was mapped to input correlithm object 2 in sensor table 308, thesecond entry of sensor output table 910 lists input correlithm object 2in column 914. Finally, because second input signal 902 b that containedreal-world value 320 b was associated with timestamp 904 b, the secondentry of sensor output table 910 lists timestamp 904 b, represented byt₂, in column 916. Sensor output table 910 may include any number ofadditional entries associated with other input signals 902 received bysensor 302. Thus, sensor output table 910 logs the inputs, outputs, andassociated timestamps of a corresponding sensor 302 to providetransparency into the operation of sensor 302 for future reference andanalysis. In particular, the sensor output table 910 supportsexamination into what inputs and outputs are associated with sensor 302over time.

Nodes 304 are configured to receive a correlithm object 104 (e.g. aninput correlithm object 104), to determine another correlithm object 104based on the received correlithm object 104, and to output theidentified correlithm object 104 (e.g. an output correlithm object 104).In one embodiment, nodes 304 are configured to use node tables 200 thatlink a plurality of correlithm objects 104 from a first n-dimensionalspace 102 with a plurality of correlithm objects 104 in a secondn-dimensional space 102. A node table 200 illustrated in FIG. 9 may beconfigured similar to the table 200 described in FIG. 2.

In general, node 304 receives the input correlithm object 104 fromsensor 302 and determines distances 106 between the input correlithmobject 104 and each source correlithm object 104 in a node table 200. Inone embodiment, the distance 106 between two correlithm objects 104 canbe determined based on the differences between the bits of the twocorrelithm objects 104. In other words, the distance 106 between twocorrelithm objects can be determined based on how many individual bitsdiffer between a pair of correlithm objects 104. The distance 106between two correlithm objects 104 can be computed using Hammingdistance, anti-Hamming distance, or any other suitable technique. Inanother embodiment, the distance 106 between two correlithm objects 104can be determined using a Minkowski distance such as the Euclidean or“straight-line” distance between the correlithm objects 104. Forexample, the distance 106 between a pair of correlithm objects 104 maybe determined by calculating the square root of the sum of squares ofthe coordinate difference in each dimension.

The node 304 identifies a source correlithm object 104 from the nodetable 200 with the shortest distance 106. A source correlithm object 104with the shortest distance from the input correlithm object 104 is acorrelithm object 104 that either matches or most closely matches thereceived input correlithm object 104. The node 304 identifies andfetches a target correlithm object 104 in the node table 200 linked withthe source correlithm object 104. The node 304 outputs the identifiedtarget correlithm object 104 to the actor 306. In this example, theidentified target correlithm object 104 is represented in the node table200 using a categorical binary integer string. The node 304 sends thebinary string representing the identified target correlithm object 104to the actor 306.

In an example operation, node 304 may receive a first input correlithmobject 104 a associated with a third timestamp 904 c where the firstinput correlithm object 104 a was determined by sensor 302 from firstreal-world value 320 a (i.e., value 3) of first input signal 902 a. Node200 may determine that first input correlithm object 104 a has theshortest n-dimensional distance to source correlithm object 1 listed incolumn 202 of node table 200, using any suitable technique explainedabove (e.g., Hamming distance of eight for 64-bit correlithm objects),and map to target correlithm object 1 listed in column 204 of node table200. Thus, node 304 may communicate target correlithm object 1 as anoutput, referenced as target correlithm object 104 c in FIG. 9.

System 900 may further include a node output table 920 that isconfigured with a first column 922 that lists source correlithm objectsdetermined by node 304 from input correlithm objects, second column 924that lists target correlithm objects that are mapped to those sourcecorrelithm objects in node table 200, third column 926 that liststimestamps 904 associated with those source correlithm objects, andfourth column 928 that lists the n-dimensional distance calculation madeby node 304 to determine a source correlithm object from the inputcorrelithm object (e.g., Hamming distance). Thus, for example, becausefirst input correlithm object 104 a was received by node 304 from sensor302 and corresponded to source correlithm object 1 in node table 200,the first entry of node output table 920 lists source correlithm object1 in column 922. Furthermore, because source correlithm object 1 wasmapped to target correlithm object 1 in node table 200, the first entryof node output table 920 lists target correlithm object 1 in column 924.Additionally, because input correlithm object 104 a that corresponded tosource correlithm object 1 was associated with timestamp 904 c, thefirst entry of node output table 920 lists timestamp 904, represented byt₃, in column 926. Finally, consider that node 304 determined a Hammingdistance of eight for the n-dimensional distance between inputcorrelithm object 3 received from sensor 302 and source correlithmobject 1 in node table 200 (e.g., assuming 64-bit correlithm objects).Accordingly, first entry of node output table 920 lists a Hammingdistance of eight in column 928.

In further operation of system 900, node 304 may receive a second inputcorrelithm object 104 b associated with a fourth timestamp 904 d wherethe second input correlithm object 104 b was determined by sensor 302from second real-world value 320 b (i.e., value 2) of second inputsignal 902 b. Node 200 may determine that second input correlithm object104 b has the shortest n-dimensional distance to source correlithmobject 3 listed in column 202 of node table 200, using any suitabletechnique explained above (e.g., Hamming distance of ten for 64-bitcorrelithm objects), and map to target correlithm object 3 listed incolumn 204 of node table 200. Thus, node 304 may communicate targetcorrelithm object 3 as an output, referenced as target correlithm object104 d in FIG. 9.

Because second input correlithm object 104 b was received by node 304from sensor 302 and corresponded to source correlithm object 3 in nodetable 200, the second entry of node output table 920 lists sourcecorrelithm object 3 in column 922. Furthermore, because sourcecorrelithm object 3 was mapped to target correlithm object 3 in nodetable 200, the second entry of node output table 920 lists targetcorrelithm object 3 in column 924. Additionally, because inputcorrelithm object 104 b that corresponded to source correlithm object 3was associated with timestamp 904 d, the second entry of node outputtable 920 lists timestamp 904 d, represented by t₄, in column 926.Finally, consider that node 304 determined a Hamming distance of ten forthe n-dimensional distance between input correlithm object 1 receivedfrom sensor 302 and source correlithm object 3 in node table 200 (e.g.,assuming 64-bit correlithm objects). Accordingly, second entry of nodeoutput table 920 lists a Hamming distance of ten in column 928. Nodeoutput table 920 may include any number of additional entries associatedwith other input correlithm objects 104 received by node 304.

Thus, node output table 920 logs the inputs, outputs, and associatedtimestamps of a corresponding node 304 to provide transparency into theoperation of node 304 for future reference and analysis. In particular,the node output table 920 supports examination into what inputs andoutputs are associated with node 304 over time. In addition, node outputtable 920 also logs the n-dimensional distance calculations (e.g.,Hamming distances) determined by node 304 to provide traceability intothe operation of node 304 for future reference and analysis. Inparticular, the node output table 920 supports examination into whycertain inputs and outputs were selected from node table 200 and usedduring the operation of the node 304 over time.

Actors 306 serve as interfaces that allow a user device 100 to convertcorrelithm objects 104 in the correlithm object domain back toreal-world values or data samples. Actors 306 enable the user device 100to convert from correlithm objects 104 into any suitable type ofreal-world value. Actors 306 are configured to receive a correlithmobject 104 (e.g. an output correlithm object 104), to determine areal-world output value 326 based on the received correlithm object 104,and to output the real-world output value 326. The real-world outputvalue 326 may be a different data type or representation of the originaldata sample. In one embodiment, actors 306 are configured to use actortables 310 that link a plurality of correlithm objects 104 in ann-dimensional space 102 with a plurality of real-world values. Asdescribed above with respect to FIG. 3, an actor table 310 may beconfigured with a first column 316 that lists correlithm objects 104 asoutput correlithm objects and a second column 318 that lists real-worldvalues. In other examples, actor tables 310 may be configured in anyother suitable manner or may be implemented using any other suitabledata structure. In some embodiments, one or more mapping functions maybe employed to translate between a correlithm object 104 in ann-dimensional space and a real-world output value 326.

In general, actor 306 receives the target correlithm object 104 anddetermines distances between the target correlithm object 104 and eachoutput correlithm object 104 in an actor table 310. The actor 306 maycompute the distances between the target correlithm object 104 and eachoutput correlithm object 104 in an actor table 310 using one of thetechniques described above. Actor 306 identifies an output correlithmobject 104 from the actor table 310 with the shortest distance 106. Anoutput correlithm object 104 with the shortest distance from the targetcorrelithm object 104 is a correlithm object 104 that either matches ormost closely matches the received target correlithm object 104.

Actor 306 identifies and fetches a real-world output value in the actortable 310 linked with the identified output correlithm object 104. Thereal-world output value may be any suitable type and format of datasample. Actor 306 outputs the identified real-world output value. In oneembodiment, the actor 306 may output the real-world output value to aperipheral device. In another embodiment, the actor 306 may output thereal-world output value to a memory or database. In still anotherembodiment, the real-world output value is sent to another sensor 302.For example, the real-world output value may be sent to another sensor302 as an input for another process.

In operation, for example, actor 306 may receive a first targetcorrelithm object 104 c associated with a fifth timestamp 904 e wherethe first target correlithm object 104 c was determined by node 304 fromfirst source correlithm object (i.e., source correlithm object 1) andfirst input correlithm object 104 a. Actor 306 may determine that firsttarget correlithm object 104 c has the shortest n-dimensional distanceto output correlithm object 2 listed in column 316 of actor table 310,using any suitable technique explained above (e.g., Hamming distance ofthree for 64-bit correlithm objects), and map to real-world value 2listed in column 318 of actor table 310. Thus, actor 306 may communicatereal-world value 2 as an output, referenced as real-world value 326 a inFIG. 9.

System 900 may further include actor output table 930 that is configuredwith a first column 932 that lists output correlithm objects determinedby node 306 from target correlithm objects, second column 934 that listsreal-world values that are mapped to those output correlithm objects inactor table 310, third column 936 that lists timestamps 904 associatedwith those output correlithm objects, and fourth column 938 that liststhe n-dimensional distance calculation made by actor 306 to determine aoutput correlithm object from the target correlithm object (e.g.,Hamming distance). Thus, for example, because first target correlithmobject 104 c was received by actor 306 from node 304 and corresponded tooutput correlithm object 2 in actor table 310, the first entry of actoroutput table 930 lists output correlithm object 2 in column 932.Furthermore, because output correlithm object 2 was mapped to real-worldvalue 2 in actor table 310, the first entry of actor output table 930lists real-world value 2 in column 934. Additionally, because targetcorrelithm object 104 c that corresponded to output correlithm object 2was associated with timestamp 904 e, the first entry of actor outputtable 930 lists timestamp 904 e, represented by t₅, in column 936.Finally, consider that actor 306 determined a Hamming distance of threefor the n-dimensional distance between target correlithm object 1received from node 304 and output correlithm object 2 in actor table 310(e.g., assuming 64-bit correlithm objects). Accordingly, first entry ofactor output table 930 lists a Hamming distance of three in column 938.

In further operation of system 900, actor 306 may receive a secondtarget correlithm object 104 d associated with a sixth timestamp 904 fwhere the second target correlithm object 104 d was determined by node304 from second source correlithm object (i.e., source correlithm object3) and second input correlithm object 104 b. Actor 306 may determinethat second target correlithm object 104 d has the shortestn-dimensional distance to output correlithm object 1 listed in column316 of actor table 310, using any suitable technique explained above(e.g., Hamming distance of seven for 64-bit correlithm objects), and mapto real-world value 1 listed in column 318 of actor table 310. Thus,actor 306 may communicate real-world value 1 as an output, referenced asreal-world value 326 b in FIG. 9.

Because second target correlithm object 104 d was received by actor 306from node 304 and corresponded to output correlithm object 1 in actortable 310, the second entry of actor output table 930 lists outputcorrelithm object 1 in column 932. Furthermore, because outputcorrelithm object 1 was mapped to real-world value 1 in actor table 310,the second entry of actor output table 930 lists real-world value 1 incolumn 934. Additionally, because target correlithm object 104 d thatcorresponded to output correlithm object 1 was associated with timestamp904 f, the second entry of actor output table 930 lists timestamp 904 f,represented by t₆, in column 936. Finally, consider that actor 306determined a Hamming distance of seven for the n-dimensional distancebetween target correlithm object 3 received from node 304 and outputcorrelithm object 1 in actor table 310 (e.g., assuming 64-bit correlithmobjects). Accordingly, second entry of actor output table 930 lists aHamming distance of seven in column 938.

Thus, actor output table 930 logs the inputs, outputs, and associatedtimestamps of a corresponding actor 306 to provide transparency into theoperation of actor 306 for future reference and analysis. In particular,the actor output table 930 supports examination into what inputs andoutputs are associated with actor 306 over time. In addition, actoroutput table 930 also logs the n-dimensional distance calculations(e.g., Hamming distances) determined by actor 306 to providetraceability into the operation of actor 306 for future reference andanalysis. In particular, the actor output table 930 supports examinationinto why certain inputs and outputs were selected from actor table 310and used during the operation of the actor 306 over time.

The sensor output table 910 captures a historical record of thereal-world values 320 received by sensor 302, the input correlithmobject output by the sensor 302, and the timestamps associated with themapping of real-world values 320 to input correlithm objects by sensor302. The node output table 920 captures a historical record of thesource correlithm objects determined by node 304, the target correlithmobjects output by the node 304, the timestamps associated with themapping of source correlithm objects with target correlithm objects bynode 304, and the n-dimensional distance calculations (e.g., Hammingdistances) associated with the operation of nodes 304. The actor outputtable 930 captures a historical record of the output correlithm objectsdetermined by actor 306, the real-world values 326 output by actor 306,the timestamps associated with the mapping of output correlithm objectsto real-world values 326 by actor 306, and the n-dimensional distancecalculations (e.g., Hamming distances) associated with the operation ofactors 306. These historical records can be individually or collectivelyretrieved and communicated in response to one or more requests in orderto perform an audit or other function. For example, where system 900 isused in an artificial intelligence engine of an autonomous vehicle thatgets into an accident, the data from one or more of sensor output table910, node output table 920, and actor output table 930 may be used toexamine the decision-making that was made by the autonomous vehicleleading to the accident. The contents of tables 910, 920, and 930 may beperiodically stored in remote memory devices for long-term storage andretrieval.

FIG. 10 is a schematic view of an embodiment of a correlithm objectprocessing system 1000 that is implemented by a user device 100 toperform operations using correlithm objects 104. System 1000 and itsconstituent components can be implemented by processor 502, one or moreof the engines 510, 512, and 514, and other elements of computerarchitecture 500, described above with respect to FIG. 5. System 1000includes sensors 302 a, 302 b, and 302 c communicatively coupled to node304. System 1000 may be configured with any suitable number and/orconfiguration of sensors 302 and nodes 304 to achieve an appropriatescale for the operation to be performed. In one embodiment, sensors 302a, 302 b, 302 c, and node 304 may all be implemented on the same device(e.g. user device 100). In other embodiments, sensors 302 a, 302 b, 302c, and node 304 may each be implemented on different devices in signalcommunication with each other, for example over a network. In otherembodiments, different devices may be configured to implement anycombination of sensors 302 a, 302 b, 302 c, and node 304.

In general, sensors 302 a-c serve as interfaces that allow a user device100 to convert real-world data samples into correlithm objects 104 thatcan be used in the correlithm object domain. Sensors 302 a-b areconfigured to receive sample text strings as real-world values and todetermine one or more correlithm objects 104 based on these values.Sensor 302 c is configured to receive a test text string as real-worldvalues and to determine one or more correlithm objects 104 based onthese values. Node 304 is configured to receive the correlithm objects104 output by sensors 302 a-c, determine which sample text string is theclosest match, in n-dimensional space, to the test text string, andoutput a correlithm object representing the determined sample textstring.

In operation, sensor 302 a receives a first sample text string 1002including a plurality of characters. In the example illustrated in FIG.10, the first sample text string 1002 is “DOG DAYS”. Sensor 302 aassigns correlithm objects 104 to subsets of the characters of the firstsample text string 1002. For example, sensor 302 a assigns a firstcorrelithm object C11 to the first two characters of the string 1002:“DO”; a second correlithm object C12 to the second and third charactersof the string 1002: “OG”; a third correlithm object C13 to the third andfourth characters of the string 1002: “G_”; a fourth correlithm objectC14 to the fourth and fifth characters of the string 1002: “_D”; a fifthcorrelithm object C15 to the fifth and sixth characters of the string1002: “DA”; a sixth correlithm object C16 to the sixth and seventhcharacters of the string 1002: “AY”; and a seventh correlithm object C17to the seventh and eighth characters of the string 1002: “YS”. Theentirety of the string 1002 may be represented by an eighth correlithmobject C1. In this way, sensor 302 a represents the real-world value of“DOG DAYS” according to pairwise combinations of characters progressingand overlapping from the beginning of the string to the end of thestring 1002. As will be described below, this allows for a more granularand accurate comparison with the test text string 1006. Although sensor302 a is described above with respect to generating correlithm objectsC11-C17 according to a successive and overlapping pairwise subset ofcharacters from string 1002, the correlithm objects 104 generated bysensor 302 a from first sample text string 1002 may be any number andcombination of characters from the string 1002 to suit particular needs.

Sensor 302 b receives a second sample text string 1004 including aplurality of characters. In the example illustrated in FIG. 10, thesecond sample text string 1004 is “DOWNTOWN”. Sensor 302 b assignscorrelithm objects 104 to subsets of the characters of the second sampletext string 1004. For example, sensor 302 b assigns a first correlithmobject C21 to the first two characters of the string 1004: “DO”; asecond correlithm object C22 to the second and third characters of thestring 1004: “OW”; a third correlithm object C23 to the third and fourthcharacters of the string 1004: “WN”; a fourth correlithm object C24 tothe fourth and fifth characters of the string 1004: “NT”; a fifthcorrelithm object C25 to the fifth and sixth characters of the string1004: “TO”; a sixth correlithm object C26 to the sixth and seventhcharacters of the string 1004: “OW”; and a seventh correlithm object C27to the seventh and eighth characters of the string 1004: “WN”. Theentirety of the string 1004 may be represented by an eighth correlithmobject C2. In this way, sensor 302 b represents the real-world value of“DOWNTOWN” according to pairwise combinations of characters progressingand overlapping from the beginning of the string to the end of thestring 1004. As will be described below, this allows for a more granularand accurate comparison with the test text string 1006. Although sensor302 b is described above with respect to generating correlithm objectsC21-C27 according to a successive and overlapping pairwise subset ofcharacters from string 1004, the correlithm objects 104 generated bysensor 302 b from second sample text string 1004 may be any number andcombination of characters from the string 1004 to suit particular needs.

Sensor 302 c receives a test text string 1006 including a plurality ofcharacters. As will be described in greater detail below, node 304 willdetermine which of the first sample text string 1002 and second sampletext string 1004 are the closest match to the test text string 1006 inn-dimensional space using correlithm objects 104. In the exampleillustrated in FIG. 10, the test text string 1006 is “BULLDOG.” Sensor302 c assigns correlithm objects 104 to subsets of the characters of thetest text string 1006. For example, sensor 302 c assigns a firstcorrelithm object T11 to the first two characters of the string 1006:“BU”; a second correlithm object T12 to the second and third charactersof the string 1006: “UL”; a third correlithm object T13 to the third andfourth characters of the string 1006: “LL”; a fourth correlithm objectT14 to the fourth and fifth characters of the string 1006: “LD”; a fifthcorrelithm object T15 to the fifth and sixth characters of the string1006: “DO”; and a sixth correlithm object T16 to the sixth and seventhcharacters of the string 1006: “OG”. The entirety of the string 1006 maybe represented by a seventh correlithm object T1. In this way, sensor302 c represents the real-world value of “BULLDOG” according to pairwisecombinations of characters progressing and overlapping from thebeginning of the string to the end of the string 1006. As will bedescribed below, this allows for a more granular and accurate comparisonbetween test text string 1006 and first and second sample text strings1002 and 1004. Although sensor 302 c is described above with respect togenerating correlithm objects T11-T16 according to a successive andoverlapping pairwise subset of characters from string 1006, thecorrelithm objects 104 generated by sensor 302 c from test text string1006 may be any number and combination of characters from the string1006 to suit particular needs.

Node 304 receives correlithm objects C1, C2, and T1 from sensors 302 a,302 b, and 302 c, respectively. In general, node 304 determines then-dimensional distances between each of the correlithm objects T11-T16of test text string 1006 against each of the correlithm objects C11-C17associated with first sample text string 1002 and stores it in distancetable 1008, an example of which is illustrated in more detail in FIG.11A. Node 304 determines a first composite value 1010 of the distancescalculated and stored in distance table 1008. Node 304 determines then-dimensional distances between each of the correlithm objects T11-T16of test text string 1006 against each of the correlithm objects C21-C27associated with second sample text string 1004 and stores it in distancetable 1012, an example of which is illustrated in more detail in FIG.11B. Node 304 determines a second composite value 1014 of the distancescalculated and stored in distance table 1012. Node 304 determines whichof first sample text string 1002 and second sample text string 1004 isthe closest match to test text string 1006 based on a comparison ofcomposite values 1010 and 1014, as described in greater detail below.

FIG. 11A illustrates one embodiment of a distance table 1008 stored inmemory 504 that is used by node 304 to compare the correlithm objectsC11-C17 of first sample text string 1002 with the correlithm objectsT11-T16 of test text string 1006 in n-dimensional space 102. Such acomparison can be used to determine how closely any portion of the firstsample text string 1002 matches the test text string 1006. In operation,node 304 compares each correlithm object T11-T16 pairwise against eachcorrelithm object C11-C17, and determines Hamming distances (oranti-Hamming distances in an alternative embodiment) based on thispairwise comparison. As described above with regard to FIG. 1, theHamming distance (or anti-Hamming distance) calculation can be used todetermine the similarity between correlithm objects T11-T16 andcorrelithm objects C11-C17. The average number of bits between a randomcorrelithm object and a particular correlithm object is equal to n/2(also referred to as standard distance), where ‘n’ is the number ofdimensions in the n-dimensional space 102. The standard deviation isequal to √{square root over (n/4)}, where ‘n’ is the number ofdimensional in the n-dimensional space 102. Thus, if a correlithm objectC11-C17 is statistically dissimilar to a corresponding correlithm objectT11-T16, then the Hamming distance is expected to be roughly equal tothe standard distance. Therefore, if the n-dimensional space 102 is64-bits, then the anti-Hamming distance between two dissimilarcorrelithm objects is expected to be roughly 32 bits. If a correlithmobject C11-C17 is statistically similar to a corresponding correlithmobject T11-T16, then the Hamming distance is expected to be roughlyequal to six standard deviations less than the standard distance.Therefore, if the n-dimensional space 102 is 64-bits, then the Hammingdistance between similar correlithm objects is expected to be eight orless (i.e., 32 (standard distance)−24 (six standard deviations)=8). Inother embodiments, if the Hamming distance is equal to four or fivestandard deviations less than the standard distance, then the correlithmobjects are determined to be statistically similar. The use of sixstandard deviations away from the standard distance to determinestatistical similarity is also appropriate in a larger n-dimensionalspace 102, such as an n-space of 256-bits.

Node 304 determines the Hamming distances between correlithm object T11and each of correlithm objects C11-C17 and stores those values in acolumn of distance table 1008 labeled “T11”. Node 304 determines theHamming distances between correlithm object T12 and each of correlithmobjects C11-C17 and stores those values in a column of distance table1008 labeled “T12”. Node 304 repeats this pairwise determination ofHamming distances between correlithm objects T13-T16 and each ofcorrelithm objects C11-C17, and stores those values in columns of table1008 labeled “T13”, “T14”, “T15”, and “T16”, respectively. The Hammingdistance values represented in the cells of table 1008 indicate whichcorrelithm objects T11-T16 of test text string 1006 are statisticallysimilar to which correlithm objects C11-C17 of first sample text string1002. Cells of table 1008 having a Hamming distance value of zero inthem, for example, indicate a similarity. Cells of table 1008 with an“SD” in them (for standard distance), for example, indicate adissimilarity. In table 1008, for example, the cell indicating theHamming distance value of zero between correlithm object T15(representing the characters “DO”) and correlithm object C11(representing characters “DO”) indicates that there is a statisticalsimilarity (“DO” is a match with “DO”). Similarly, the cell indicatingthe Hamming distance value of zero between correlithm object T16(representing the characters “OG”) and correlithm object C12(representing characters “OG”) indicates that there is a statisticalsimilarity (“OG” is a match with “OG”). The combination of thesecomparisons between successive correlithm objects (T15-T16 and C11-C12)also indicates that the combination of the characters represented bythose correlithm objects are statistically similar (“DOG” is a matchwith “DOG”). The Hamming distances represented by the cells of table1008 can be added together to form an aggregate Hamming distance 1010(e.g., aggregate Hamming distance calculation of 1280 for table 1008).The smaller the aggregate Hamming distance calculation, the closer thestatistical similarity between correlithm objects T11-16 and correlithmobjects C11-C17. By representing subsets of characters in text stringsin n-dimensional space using correlithm objects and then comparing thosecorrelithm to each other as described above with respect to table 1008,system 1000 can find statistical similarities among text strings.

FIG. 11B illustrates one embodiment of a distance table 1012 stored inmemory 504 that is used by node 304 to compare the correlithm objectsC21-C27 of second sample text string 1004 with the correlithm objectsT11-T16 of test text string 1006 in n-dimensional space 102. Such acomparison can be used to determine how closely any portion of thesecond sample text string 1004 matches the test text string 1006. Inoperation, node 304 compares each correlithm object T11-T16 pairwiseagainst each correlithm object C21-C27, and determines Hamming distances(or anti-Hamming distances in an alternative embodiment) based on thispairwise comparison. As described above, if a correlithm object C21-C27is statistically dissimilar to a corresponding correlithm objectT11-T16, then the Hamming distance is expected to be roughly equal tothe standard distance. If a correlithm object C21-C27 is statisticallysimilar to a corresponding correlithm object T11-T16, then the Hammingdistance is expected to be roughly equal to the six standard deviationsless than the standard distance (e.g., Hamming distance of eight for64-bit n-dimensional space 102).

Node 304 determines the Hamming distances between correlithm object T11and each of correlithm objects C21-C27 and stores those values in acolumn of distance table 1012 labeled “T11”. Node 304 determines theHamming distances between correlithm object T12 and each of correlithmobjects C21-C27 and stores those values in a column of distance table1012 labeled “T12”. Node 304 repeats this pairwise determination ofHamming distances between correlithm objects T13-T16 and each ofcorrelithm objects C21-C27, and stores those values in columns of table1008 labeled “T13”, “T14”, “T15”, and “T16”, respectively. The Hammingdistance values represented in the cells of table 1008 indicate whichcorrelithm objects T11-T16 of test text string 1006 are statisticallysimilar to which correlithm objects C21-C27 of second sample text string1004. Cells of table 1012 having a Hamming distance value of zero inthem, for example, indicate a similarity. Cells of table 1012 with an“SD” in them (for standard distance), for example, indicate adissimilarity. In table 1012, for example, the cell indicating theHamming distance value of zero between correlithm object T15(representing the characters “DO”) and correlithm object C21(representing characters “DO”) indicates that there is a statisticalsimilarity (“DO” is a match with “DO”). Unlike with correlithm objectsC11-C17 representing first sample text string 1002, none of the othercorrelithm objects T11-T16 are statistically similar to any othercorrelithm objects C21-C27. The Hamming distances represented by thecells of table 1012 can be added together to form an aggregate Hammingdistance 1014 (e.g., aggregate Hamming distance calculation of 1312 fortable 1012). The smaller the aggregate Hamming distance calculation, thecloser the statistical similarity between correlithm objects T11-16 andcorrelithm objects C21-C27. By representing subsets of characters intext strings in n-dimensional space using correlithm objects and thencomparing those correlithm to each other as described above with respectto table 1012, system 1000 can find statistical similarities among textstrings.

Node 304 compares the aggregate Hamming distance 1010 of table 1008(e.g., 1280) with the aggregate Hamming distance 1014 of table 1012(e.g., 1312) to determine which is smaller to indicate which of thefirst sample text string 1002 or the second sample text string 1004 ismore statistically similar to the test text string 1006. In thisexample, because the aggregate Hamming distance 1010 associated withfirst sample text string 1002 is smaller than the aggregate Hammingdistance 1014 associated with the second sample text string 1004, node304 determines that first sample text string 1002 is a closer match totest text string 1006 than second sample text string 1004. This resultis supported by the fact that first sample text string 1002 and testtext string 1006 have common characters that spell “DOG” whereas thesecond sample text string 1004 and test text string 1006 only havecommon characters that spell “DO”. Accordingly, node 304 outputs acorrelithm object C1 representing the first sample text string 1002.

FIG. 12 is a schematic view of an embodiment of a correlithm objectprocessing system 1200 that is implemented by a user device 100 toperform operations using correlithm objects 104. The system 1200 is avariation of the system 300 illustrated in FIG. 3 and can be implementedby processor 502, one or more engines 510, 512, and 514, and otherelements of computer architecture 500, described above with respect toFIG. 5. As with system 300, system 1200 may be configured with anysuitable number and/or configuration of sensors 302, nodes 304, andactors 306. As illustrated in FIG. 12, a collection of sensors 302 maybe communicatively coupled to a collection of nodes 304, which may befurther communicatively coupled to a collection of actors 306. Thecollection of sensors 302 have access to one or more sensor tables 308to perform various functions associated with mapping real-world inputvalues 320 to input correlithm objects 104, among others, as describedabove with respect to FIGS. 1-11. The collection of nodes 304 haveaccess to one or more node tables 200 to perform various functionsassociated with mapping input correlithm objects 104 to outputcorrelithm objects 104, among others, as described above with respect toFIGS. 1-11. The collection of actors 306 have access to one or moreactor tables 310 to perform various functions associated with mappingoutput correlithm objects 104 to real-world output values 326, amongothers, as described above with respect to FIGS. 1-11.

System 1200 illustrated in FIG. 12 adds mobility and collectiveprocessing to the sensors 302, nodes 304, and actors 306, as describedin greater detail below. In particular, system 1200 includes anysuitable number and combination of mobile correlithm object devices 1210(e.g., mobile correlithm object devices 1210 a-f described below) thateach comprise correlithm object “parts” such as sensors 302, nodes 304,and/or actors 306, or higher-level aggregates of such parts, where suchaggregates fulfill higher-level correlithm object functions (e.g., suchas language or facial recognition or robotic control, among others). Amobile correlithm object device 1210 can be implemented by processor502, one or more engines 510, 512, and 514, and other elements ofcomputer architecture 500, described above with respect to FIG. 5.

Each mobile correlithm object device 1210 functions as a single organismwhile maintaining continuous or periodic communication with the otherelements of system 1200 as a whole. In various embodiments, the mobilecorrelithm object devices 1210 may communicate with each other, usinglight signals, audio signals, radio frequency signals, all or a portionof a telecommunications network (e.g., Internet), or using any othersuitable communications technique applicable to computer or networkingsystems. System 1200 further includes a central command module 1220 thatcommunicates with and functions as an executive director for anaffiliated group of mobile correlithm object devices 1210. Processor 502of computer architecture 500 is configured to implement central commandmodule 1220.

A mobile correlithm object device 1210 has the ability to communicatewith other mobile correlithm object devices 1210. In one embodiment, agroup of mobile correlithm object devices 1210 are affiliated with oneanother by being assigned common tasks to perform. In anotherembodiment, a group of mobile correlithm object devices 1210 areunaffiliated with one another by being assigned different tasks toperform. A mobile correlithm object device 1210 has the ability tocommunicate with other mobile correlithm object devices 1210 whetherthey are affiliated or unaffiliated. Each mobile correlithm objectdevice 1210 has sufficient intelligence and functionality to performindividual tasks as a part of a collective function performed by itsaffiliated group of mobile correlithm object devices 1210. For example,a collection of mobile correlithm object devices 1210 can collectivelyimplement some or all portions of a computer program in a computersystem. The collective intelligence and functionality of the mobilecorrelithm object devices 1210 and their associated central commandmodule 1220 is distributed across the entire aggregate system 1200 andall or most of its components. One common term for this kind oforganization is “swarm intelligence” which is a collective behavior ofdecentralized, self-organized components.

FIG. 12 illustrates example mobile correlithm object devices 1210 a-fwhich are now described herein. A mobile correlithm object device 1210 amay embody a sensor 302 (indicated by “S”) from the collection ofsensors 302 illustrated in FIG. 12. This means that device 1210 a canperform at least the functionalities of a sensor 302, as describedabove. Device 1210 a includes an address 1212 a and a destination table1214 a. The address 1212 a is any suitable logical or physicalidentifier for device 1210 a, such as a network address, a MAC address,an IP address, a computer address, or any other suitable communicationsaddress. Destination table 1214 a is a physical or logical datastructure that identifies other mobile correlithm objects 1210 withwhich to connect and communicate information, and their respectiveaddresses 1214. Although only a single mobile correlithm object device1210 a is prominently illustrated and described in FIG. 12, system 1200can include and deploy any suitable number and combination of mobilecorrelithm object devices 1210 a.

In another example, mobile correlithm object device 1210 b may embody anode 304 (indicated by “N”) from the collection of nodes 304 illustratedin FIG. 12. This means that device 1210 b can perform at least thefunctionalities of a node 304, as described above. Device 1210 bincludes an address 1212 b and a destination table 1214 b which aresimilar to address 1212 a and destination table 1212 a but are specificto mobile correlithm object device 1210 b. Although only a single mobilecorrelithm object device 1210 b is prominently illustrated and describedin FIG. 12, system 1200 can include and deploy any suitable number andcombination of mobile correlithm object devices 1210 b.

In still another example, mobile correlithm object device 1210 c mayembody an actor 306 (indicated by “A”) from the collection of actors 306illustrated in FIG. 12. This means that device 1210 c can perform atleast the functionalities of an actor 306, as described above. Device1210 c includes an address 1212 c and a destination table 1214 c whichare similar to address 1212 a and destination table 1212 a but arespecific to mobile correlithm object device 1210 c. Although only asingle mobile correlithm object device 1210 c is prominently illustratedand described in FIG. 12, system 1200 can include and deploy anysuitable number and combination of mobile correlithm object devices 1210c.

Particular mobile correlithm objects 1210 may embody a combination ofsensors 302, nodes 304, and actors 306. For example, mobile correlithmobject device 1210 d may embody each of a sensor 302 and node 304(indicated by “SN”) from the collection of sensors 302 and nodes 304illustrated in FIG. 12. This means that device 1210 d can perform thefunctionalities of at least both sensors 302 and nodes 304, as describedabove. Device 1210 d includes an address 1212 d and a destination table1214 d which are similar to address 1212 a and destination table 1212 abut are specific to mobile correlithm object device 1210 d. Althoughonly a single mobile correlithm object device 1210 d is prominentlyillustrated and described in FIG. 12, system 1200 can include and deployany suitable number and combination of mobile correlithm object devices1210 d.

In another example, mobile correlithm object device 1210 e may embodyeach of a sensor 302, node 304, and an actor 306 (indicated by “SNA”)from the collection of sensors 302, nodes 304, and actors 306illustrated in FIG. 12. This means that device 1210 e can perform thefunctionalities of at least each of sensors 302, nodes 304, and actors306, as described above. Device 1210 e includes an address 1212 e and adestination table 1214 e which are similar to address 1212 a anddestination table 1212 a but are specific to mobile correlithm objectdevice 1210 e. Although only a single mobile correlithm object device1210 e is prominently illustrated and described in FIG. 12, system 1200can include and deploy any suitable number and combination of mobilecorrelithm object devices 1210 e.

In still another example, mobile correlithm object device 1210 f mayembody each of a node 304 and an actor 306 (indicated by “NA”) from thecollection of nodes 304 and actors 306 illustrated in FIG. 12. Thismeans that device 1210 f can perform the functionalities of at leastboth of nodes 304 and actors 306, as described above. Device 1210 fincludes an address 1212 f and a destination table 1214 f which aresimilar to address 1212 a and destination table 1212 a but are specificto mobile correlithm object device 1210 f. Although only a single mobilecorrelithm object device 1210 f is prominently illustrated and describedin FIG. 12, system 1200 can include and deploy any suitable number andcombination of mobile correlithm object devices 1210 f.

In some embodiments, one or more of devices 1210 are mobile in somecapacity such as, for example, physically, logically, or otherwise. Inthis context, mobility may be reflected as physical separation anddeployment away from the other elements of system 1200, includingcentral command module 1220. Therefore, in a collective system 1200, themobile correlithm object devices 1210 can be deployed across a logicalor physical space instead of or in addition to being located as asingle, local computational module. One beneficial environment formobile correlithm object devices 1210 to be deployed is the Internet.For example, correlithm object based mobile devices embodying sensors,nodes, and/or actors could be distributed across the Internet, withcomponents lying in different machines, servers, physical or logicalrobots, or other suitable constructs. Another beneficial environment formobile correlithm object devices 1210 to be deployed is within differentelements of a computer system. Such deployment of mobile correlithmobject devices 1210 may provide technical advantages in the form ofsignificant and robust resistance to physical damage to any host systembecause the elements of system 1200, in this case, would not necessarilyreside in only one specific locality.

Mobility may also be reflected as logical separation. The mobilecorrelithm object devices 1210 could be dispersed across a network, sothat its functionalities are ubiquitous. Such mobile correlithm objectdevices 1210 could be shifted from logical host system 1200 to anotherlogical host system 1200 piecemeal, with each mobile correlithm objectdevice 1210 itself remaining functionally intact and able to communicateeffectively with other mobile correlithm object devices 1210, asappropriate.

The design and implementation of individual mobile correlithm objectdevices 1210 could range across a spectrum from individual components tocomplex subsystems akin to “lobes,” which are higher-level aggregatefunctionalities and functional units of correlithm object systems. Thisapproach could accomplish several technical advantages. For example, aphysical machine might have a correlithm object-based intelligence thatis in fact a subordinate component of the collective functionality, withenough logically local functionality to deal with specific localsituations. Such an architecture would minimize response and reflextime. The actions of an individual machine would be communicated to ageneral central correlithm object system, such as central command module1220, for further evaluation and direction. Each mobile correlithmobject device 1210 could be designed to meet a specific purpose. Thesepurposes span many dimensions, such as, for example, mobility choices(e.g., where and how to move next), action choices (e.g., “fight orflee”), coordination choices (e.g., if, and how best to cooperate andsupport nearby groupings of mobile correlithm object devices 1210),communications choices (e.g., how much and what to pass on to a centralcorrelithm object system or other mobile correlithm object devices1210), and others.

One embodiment of these mobile correlithm object devices 1210 is“containerized” mobile correlithm object devices 1210. In thisembodiment, each mobile correlithm object device 1210, or perhaps acollection of them, and other components or even the entire centralcorrelithm object system 1200 (as is appropriate for the task at hand)are logically placed into a “container.” In this context, a container isan operating system feature in which the kernel allows the existence ofmultiple isolated user-space instances. A containerized environment,such as this, is good for the distribution of the mobile correlithmobject devices 1210 across a network in any suitable manner for the taskat hand. In this way, mobile correlithm object devices 1210 that embodyone or more of sensors 302, nodes 304, and actors 306 can be deployedacross a network.

System 1200 that deploys mobile correlithm object devices 1210 asdescribed above add redundancy and security against the damage or lossof any given mobile device 1210. In some embodiments, a subset of mobilecorrelithm objects 1210 are deployed temporarily, akin to sending out ascouting party of mobile devices 1210 instead of individual mobiledevices 1210. Furthermore, system 1200 facilitates reproduction of theentire system 1200 or particular parts of it (e.g., particular mobiledevices 1210) to add to the robustness of the system 1200. System 1200similarly provides techniques for growth of the correlithm object systemor its component parts at any level from simple individual mobilecorrelithm object devices 1210, to more complex mobile correlithm objectdevices 1210, to a central correlithm object system itself, by addingparts to the system according to several strategies (cloning, addingvirgin (untrained) parts, generating new virgin parts, etc.).

In operation, one or more mobile correlithm object devices 1210, such asany number and combination of mobile correlithm object devices 1210 a-f,are deployed to different parts of a network or system to performparticular assigned tasks. Each of the deployed mobile correlithm objectdevices 1210 perform one or more of the functionalities of a sensor 302,node 304, and/or actor 306, as described above with respect to FIGS.1-11. In one embodiment, one or more of the deployed mobile correlithmobject devices 1210 communicate with one another using, for example,their assigned addresses 1212 and destination tables 1214. In anotherembodiment, one or more of the deployed mobile correlithm object devices1210 communicate with other nearby deployed mobile correlithm objectdevices 1210 regardless of their destination tables 1214. Mobilecorrelithm object devices 1210 periodically report back to and takeinstruction from central command module 1220 to coordinate theirfunctionality, deployment, and other activities.

While several embodiments have been provided in the present disclosure,it should be understood that the disclosed systems and methods might beembodied in many other specific forms without departing from the spiritor scope of the present disclosure. The present examples are to beconsidered as illustrative and not restrictive, and the intention is notto be limited to the details given herein. For example, the variouselements or components may be combined or integrated in another systemor certain features may be omitted, or not implemented.

In addition, techniques, systems, subsystems, and methods described andillustrated in the various embodiments as discrete or separate may becombined or integrated with other systems, modules, techniques, ormethods without departing from the scope of the present disclosure.Other items shown or discussed as coupled or directly coupled orcommunicating with each other may be indirectly coupled or communicatingthrough some interface, device, or intermediate component whetherelectrically, mechanically, or otherwise. Other examples of changes,substitutions, and alterations are ascertainable by one skilled in theart and could be made without departing from the spirit and scopedisclosed herein.

To aid the Patent Office, and any readers of any patent issued on thisapplication in interpreting the claims appended hereto, applicants notethat they do not intend any of the appended claims to invoke 35 U.S.C. §112(f) as it exists on the date of filing hereof unless the words “meansfor” or “step for” are explicitly used in the particular claim.

1. A device configured to emulate a correlithm object processing system, comprising: a memory operable to store a node table that identifies: a plurality of source correlithm objects, wherein each source correlithm object is a point in a first n-dimensional space represented by a binary string; and a plurality of target correlithm objects, wherein: each target correlithm object is a point in a second n-dimensional space represented by a binary string, and each target correlithm object is linked with a source correlithm object from among the plurality of source correlithm objects; a node operably coupled to the memory, configured to: receive an input correlithm object; compute distances between the input correlithm object and each of the source correlithm objects in the node table in response to receiving the input correlithm object, wherein the distance between the input correlithm object and a source correlithm object is determined based on differences between a binary string representing the input correlithm object and binary strings representing each of the source correlithm objects; identify a source correlithm object from the node table with the shortest distance; identify a first target correlithm object from the node table linked with the identified source correlithm object; generate a second target correlithm object that is offset in the second n-dimensional space from the first target correlithm object by the distance in the first n-dimensional space between the input correlithm object and the identified source correlithm object; and output the second target correlithm object.
 2. The device of claim 1, wherein the first n-dimensional space and the second n-dimensional space have the same number of dimensions.
 3. The device of claim 1, wherein the first n-dimensional space and the second n-dimensional space have different numbers of dimensions.
 4. The device of claim 1, wherein determining distances between the input correlithm object and each of the source correlithm objects in the node table comprises determining a Hamming distance between the input correlithm object and a source correlithm object.
 5. The device of claim 4, wherein: the first target correlithm object comprises a binary string; the second target correlithm object comprises the binary string of the first target correlithm object with a particular number of bits in the binary string changed from a zero to a one or from a one to a zero; and the particular number of bits that are changed is equal to the Hamming distance between the input correlithm object and the source correlithm object.
 6. The device of claim 1, wherein the node engine is configured to output the target correlithm object to an actor engine configured to convert the target correlithm object into a real-world output value.
 7. The device of claim 1, wherein the node engine is configured to receive the input correlithm object from a sensor engine configured to convert a real-world value into the input correlithm object.
 8. A method for emulating a node in a correlithm object processing system, comprising: storing a node table that identifies: a plurality of source correlithm objects, wherein each source correlithm object is a point in a first n-dimensional space represented by a binary string; and a plurality of target correlithm objects, wherein: each target correlithm object is a point in a second n-dimensional space represented by a binary string, and each target correlithm object is linked with a source correlithm object from among the plurality of source correlithm objects; receiving, by a node engine, an input correlithm object; determining, by the node engine, distances between the input correlithm object and each of the source correlithm objects in the node table in response to receiving the input correlithm object, wherein the distance between the input correlithm object and a source correlithm object is determined based on differences between a binary string representing the input correlithm object and binary strings representing each of the source correlithm objects; identifying, by the node engine, a source correlithm object from the node table with the shortest distance; identifying, by the node engine, a target correlithm object from the node table linked with the identified source correlithm object; generating a second target correlithm object that is offset in the second n-dimensional space from the first target correlithm object by the distance in the first n-dimensional space between the input correlithm object and the identified source correlithm object; and and outputting, by the node engine, the second target correlithm object.
 9. The method of claim 8, wherein the first n-dimensional space and the second n-dimensional space have the same number of dimensions.
 10. The method of claim 8, wherein the first n-dimensional space and the second n-dimensional space have different numbers of dimensions.
 11. The method of claim 8, wherein determining distances between the input correlithm object and each of the source correlithm objects in the node table comprises determining a Hamming distance between the input correlithm object and a source correlithm object.
 12. The method of claim 11, wherein: the first target correlithm object comprises a binary string; the second target correlithm object comprises the binary string of the first target correlithm object with a particular number of bits in the binary string changed from a zero to a one or from a one to a zero; and the particular number of bits that are changed is equal to the Hamming distance between the input correlithm object and the source correlithm object.
 13. The method of claim 8, wherein the node engine is configured to output the target correlithm object to an actor engine configured to convert the target correlithm object into a real-world output value.
 14. The method of claim 8, wherein the node engine is configured to receive the input correlithm object from a sensor engine configured to convert a real-world value into the input correlithm object.
 15. A computer program product comprising executable instructions stored in a non-transitory computer readable medium such that when executed by a processor causes the processor to emulate a node in a correlithm object processing system configured to: store a node table that identifies: a plurality of source correlithm objects, wherein each source correlithm object is a point in a first n-dimensional space represented by a binary string; and a plurality of target correlithm objects, wherein: each target correlithm object is a point in a second n-dimensional space represented by a binary string, and each target correlithm object is linked with a source correlithm object from among the plurality of source correlithm objects; receive, by a node engine, an input correlithm object; determine, by the node engine, distances between the input correlithm object and each of the source correlithm objects in the node table in response to receiving the input correlithm object, wherein the distance between the input correlithm object and a source correlithm object is determined based on differences between a binary string representing the input correlithm object and binary strings representing each of the source correlithm objects; identify, by the node engine, a source correlithm object from the node table with the shortest distance; identify, by the node engine, a target correlithm object from the node table linked with the identified source correlithm object; generate a second target correlithm object that is offset in the second n-dimensional space from the first target correlithm object by the distance in the first n-dimensional space between the input correlithm object and the identified source correlithm object; and and output, by the node engine, the second target correlithm object.
 16. The computer program product of claim 15, wherein the first n-dimensional space and the second n-dimensional space have the same number of dimensions.
 17. The computer program product of claim 15, wherein the first n-dimensional space and the second n-dimensional space have different numbers of dimensions.
 18. The computer program of claim 15, wherein determining distances between the input correlithm object and each of the source correlithm objects in the node table comprises determining a Hamming distance between the input correlithm object and a source correlithm object.
 19. The computer program of claim 15, wherein: the first target correlithm object comprises a binary string; the second target correlithm object comprises the binary string of the first target correlithm object with a particular number of bits in the binary string changed from a zero to a one or from a one to a zero; and the particular number of bits that are changed is equal to the Hamming distance between the input correlithm object and the source correlithm object. 